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Single Technology Appraisal

Polatuzumab vedotin in combination for untreated diffuse large B-cell lymphoma [ID3901]

Committee Papers

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NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE

SINGLE TECHNOLOGY APPRAISAL

Polatuzumab vedotin in combination for untreated diffuse large B-cell lymphoma [ID3901]

Contents:

The following documents are made available to consultees and commentators:

The final scope and final stakeholder list are available on the NICE website.

1. Company submission from Roche

2. Clarification questions and company responses

3. Patient group, professional group and NHS organisation submission from:

• a. Lymphoma Action

4. Evidence Review Group report prepared by Southampton Health Technology Assessments Centre (SHTAC)

5. Technical engagement response from Roche

6. Technical engagement responses & expert statements from experts: a. Chris Fox, clinical expert – nominated by NCRI-ACP-RCP-RCR

• b. Wendy Osborne, clinical expert – nominated by Roche

7. Evidence Review Group critique of company response to technical engagement prepared by Southampton Health Technology Assessments Centre (SHTAC)

Any information supplied to NICE which has been marked as confidential, has been redacted. All personal information has also been redacted.

NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE

Single technology appraisal

Polatuzumab vedotin in combination with R-CHP for untreated diffuse large B-cell lymphoma [ID3901]

Document B

Company evidence submission

March 2022

Company evidence submission for polatuzumab vedotin in combination with R-CHP for untreated diffuse large B-cell lymphoma [ID3901]

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Contents

Contents ................................................................................................................... 2 Tables and figures .................................................................................................... 5 Abbreviations ........................................................................................................... 7 B.1 Decision problem, description of the technology and clinical care pathway ..... 10 B.1.1 Decision problem .......................................................................................... 10 B.1.2 Description of the technology being appraised ............................................. 12 B.1.3 Health condition and position of the technology in the treatment pathway ... 14 B.1.3.1 Disease overview ....................................................................................... 14 B.1.3.2 Current clinical practice in the UK .............................................................. 20 B.1.3.3 Disease management pathway .................................................................. 22 B.1.4 Equality considerations ................................................................................. 23 B.2 Clinical effectiveness ....................................................................................... 23 B.2.1 Identification and selection of relevant studies .............................................. 23 B.2.2 List of relevant clinical effectiveness evidence .............................................. 23 B.2.3 Summary of methodology of the relevant clinical effectiveness evidence .... 24 B.2.3.1 Study methodology .................................................................................... 24 Study design ...................................................................................................... 24 Inclusion/exclusion criteria ................................................................................. 24 Randomisation and cycles of treatment ............................................................. 25 Assessment ....................................................................................................... 25 B.2.3.2 Endpoints and assessments ...................................................................... 26 Primary efficacy endpoint ................................................................................... 26 Secondary efficacy endpoints ............................................................................ 26 Exploratory endpoints ........................................................................................ 26 B.2.3.3 Patient demographics and baseline characteristics ................................... 27 B.2.4 ........... Statistical analysis and definition of study groups in the relevant clinical effectiveness evidence ......................................................................................... 29 Determination of sample size ............................................................................. 29 Efficacy analyses ............................................................................................... 30 B.2.5 Quality assessment of the relevant clinical effectiveness evidence .............. 31 B.2.6 Clinical effectiveness results of the relevant trials ......................................... 33 B.2.6.1 Primary efficacy endpoint: progression-free survival (PFS) ....................... 33 B.2.6.2 Secondary efficacy endpoints .................................................................... 34 Investigator-assessed event-free survival (EFSeff) ............................................. 35 BICR-assessed complete response rate at end of treatment (by PET-CT) ........ 36 Overall survival (OS) .......................................................................................... 36 New anti-lymphoma treatment (NALT) ............................................................... 37 Additional secondary efficacy endpoints (not formally tested) ........................... 38 Investigator-assessed duration of response (DOR) and best overall response (BOR) ............................................................................................................... 39 Investigator-assessed disease-free survival (DFS) ............................................ 39

Company evidence submission for polatuzumab vedotin in combination with R-CHP for untreated diffuse large B-cell lymphoma [ID3901]

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B.2.7 Subgroup analysis ........................................................................................ 39 B.2.8 Meta-analysis ................................................................................................ 42 B.2.9 Indirect and mixed treatment comparisons ................................................... 42 B.2.10 Adverse reactions ....................................................................................... 42 B.2.10.1 Overview of safety .......................................................................... 42 Exposure to study treatment .............................................................................. 44 B.2.10.2 Adverse events (AEs) ..................................................................... 45 Serious adverse events (SAEs) ......................................................................... 46 Adverse events of particular interest (AEPIs) .................................................... 47 Deaths ............................................................................................................... 48 Safety conclusion ............................................................................................... 49 B.2.11 Ongoing studies .......................................................................................... 50 B.2.12 Innovation ................................................................................................... 50 Mode of action ................................................................................................... 50 Unmet need ....................................................................................................... 51 B.2.13 Interpretation of clinical effectiveness and safety evidence ........................ 53 B.3 Cost effectiveness ........................................................................................... 55 B.3.1 Published cost-effectiveness studies ............................................................ 56 B.3.2 Economic analysis ........................................................................................ 56 B.3.2.1 Patient population ...................................................................................... 56 B.3.2.2 Model structure .......................................................................................... 56 B.3.2.3 Intervention technology and comparators .................................................. 62 B.3.3 Clinical parameters and variables ................................................................. 62 B.3.3.1 Survival inputs and assumptions ............................................................... 62 Cure mixture model ............................................................................................ 64 B.3.3.2 Extrapolation of PFS .................................................................................. 64 B.3.3.3. Extrapolation of OS ................................................................................... 69 B.3.3.4. Base case extrapolations of PFS and OS ................................................. 76 B.3.3.5 Treatment duration in the economic model ................................................ 77 B.3.3.6 Adverse events .......................................................................................... 79 B.3.4 Measurement and valuation of health effects ............................................... 79 B.3.4.1 Health-related quality-of-life data from clinical trials ................................... 80 B.3.4.2 Mapping ..................................................................................................... 80 B.3.4.3 Health-related quality-of-life studies ........................................................... 80 B.3.4.4 Adverse reactions ...................................................................................... 80 B.3.4.5 Health-related quality-of-life data used in the cost-effectiveness analysis . 81 B.3.5 Cost and healthcare resource use identification, measurement and valuation ............................................................................................................................. 83 B.3.5.1 Intervention and comparators’ costs and resource use.............................. 84 B.3.5.2 Health-state unit costs and resource use ................................................... 88 B.3.5.3 Adverse reaction unit costs and resource use ........................................... 92 B.3.5.4 Subsequent treatment costs ...................................................................... 93 B.3.5.5 Miscellaneous unit costs and resource use ............................................... 95

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B.3.6 Summary of base-case analysis inputs and assumptions............................. 95 B.3.6.1 Summary of base case analysis inputs ...................................................... 95 B.3.6.2 Assumptions .............................................................................................. 98 B.3.7 Base-case results ....................................................................................... 100 B.3.7.1 Base case incremental cost-effectiveness analysis results...................... 100 B.3.8 Sensitivity analyses .................................................................................... 100 B.3.8.1 Probabilistic sensitivity analysis ............................................................... 100 B.3.8.2 Deterministic sensitivity analysis .............................................................. 104 B.3.8.3 Scenario analysis ..................................................................................... 107 B.3.8.4 Summary of sensitivity analyses results .................................................. 110 B.3.9 Subgroup analysis ...................................................................................... 110 B.3.10 Validation .................................................................................................. 110 B.3.10.1 Validation of cost-effectiveness analysis ................................................ 110 B.3.11 Interpretation and conclusions of economic evidence .............................. 111 B.4 References .................................................................................................... 113

Company evidence submission for polatuzumab vedotin in combination with R-CHP for untreated diffuse large B-cell lymphoma [ID3901]

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Tables and figures

Table 1: The decision problem ................................................................................. 11 Table 2: Technology being appraised ...................................................................... 12 Table 3: Ann Arbor Staging System for lymphoma .................................................. 15 Table 4: Treatment outcomes in DLBCL .................................................................. 17 Table 5: Clinical effectiveness evidence ................................................................... 23 Table 6: Summary of key demographic data and disease characteristics at baseline ................................................................................................................................. 27 Table 7: Patient disposition – intention-to-treat (ITT) population .............................. 29 Table 8: Risk of bias assessment for POLARIX ....................................................... 32 Table 8: Summary of investigator-assessed PFS (ITT population) .......................... 33 Table 9: Key secondary efficacy endpoint results for pola (ITT population) ......... 34 Table 10: Follow-up anti-lymphoma treatments (ITT population) ............................. 38 Table 11: Other selected secondary endpoints (not formally tested) ....................... 38 Table 12: Investigator-assessed PFS by subgroup (unstratified) ............................. 40 Table 13: POLARIX safety profile (safety-evaluable population) .............................. 44 Table 14: Summary of AEs with an incidence rate of at least 10% by preferred term (safety-evaluable population) ................................................................................... 45 Table 15: Summary of SAEs with an incidence rate of at least 1% (safety-evaluable population) ............................................................................................................... 47 Table 16: Features of the economic analysis ........................................................... 61 Table 17: Ranking of PFS distribution for Pola+R-CHP and R-CHOP based on AIC, BIC, long-term survival estimate and assessment of their visual fit. ......................... 67 Table 18: Ranking of OS distribution for Pola+R-CHP and R-CHOP based on AIC, BIC, long-term survival estimate and assessment of their visual fit .......................... 72 Table 19: Time-to-off treatment in months for Pola+R-CHP and R-CHOP (POLARIX) ................................................................................................................................. 78 Table 20: Average treatment cycles for Pola+R-CHP and R-CHOP (POLARIX) ..... 79 Table 21: POLARIX adverse events included in the economic model (events occurring at Grade 3–5, affecting 2% or more of patients) ....................................... 79 Table 22: Summary of adverse events and disutilities included in the economic model (events occurring at Grade 3–5, affecting 2% or more of patients) ............... 81 Table 23: Summary of utility values for cost-effectiveness analysis ......................... 82 Table 24: Summary of utility values for cost-effectiveness analysis (scenario analyses) .................................................................................................................. 83 Table 25: Treatment acquisition costs (with PAS) .................................................... 86 Table 26: NHS reference costs 2019–20 for chemotherapy administration ............. 87 Table 27: Drug administration costs per cycle .......................................................... 87 Table 28: Supportive care resource use unit costs included in the model ................ 88 Table 29: Annual frequency of resource use in PFS (on and off treatment) and PD 90 Table 30: Per cycle supportive care costs for PFS and PD health states ................. 92 Table 31: Resources associated with adverse events.............................................. 93 Table 32: Subsequent systemic treatment (UK clinical input) .................................. 94 Table 33: Number of subsequent systemic treatments after 1L ............................... 94 Table 34: SCT cost inputs ........................................................................................ 94 Table 35: Subsequent treatment administration costs.............................................. 95 Table 36: Subsequent treatment costs based on POLARIX data ............................. 95 Table 37: Summary of variables applied in the economic model base case ............ 96

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Table 38: Key assumptions used in the economic model......................................... 98 Table 39: Base case results (with PAS for POLIVY) .............................................. 100 Table 40: PSA parameter inputs ............................................................................ 101 Table 41: Mean probabilistic results (with PAS) ..................................................... 103 Table 42: Parameter values used for DSA (with PAS for POLIVY) ........................ 105 Table 43: Parameters varied in the scenario analysis ............................................ 107 Table 44: Parameters varied in the scenario analysis ............................................ 107 Figure 1: Current treatment pathway for adult patients (aged 18-80) with previously untreated DLBCL (including Pola+R-CHP positioning) ............................................ 22 Figure 2: Overview of the design of POLARIX (study GO39942) (53) ..................... 24 Figure 3: POLARIX statistical analysis plan ............................................................. 30 Figure 4: Kaplan-Meier plot of time to investigator-assessed PFS (ITT population) (53) ........................................................................................................................... 34 Figure 5: Kaplan-Meier plot of investigator-assessed EFSeff (ITT population) (53) .. 36 Figure 6: Kaplan-Meier plot of time to overall survival (ITT population) (53) ............ 37 Figure 7: Polatuzumab vedotin molecule ................................................................. 50 Figure 8: Mechanism of action of antibody-drug conjugates .................................... 51 Figure 9: Economic model structure ......................................................................... 57 Figure 10: Example of a partitioned survival model .................................................. 58 Figure 11: Age distribution in the POLARIX trial ...................................................... 59 Figure 12: Comparison of the general survival of cohort of 63 years only versus a cohort having the same age distribution as in POLARIX .......................................... 60 Figure 13: Visual check of PH assumption - log-cumulative hazard for INV-PFS IPI 2–5 ........................................................................................................................... 64 Figure 14: KM plot for INV-PFS (POLARIX; data cut: June 2021) ........................... 65 Figure 15: PFS cure mixture extrapolation functions - Pola+R-CHP ........................ 68 Figure 16: PFS cure mixture extrapolation functions - R-CHOP .............................. 68 Figure 17: PFS mixture-cure extrapolation functions – +Pola+R-CHP and R-CHOP69 Figure 18: Visual check of PH assumption - KM OS ................................................ 69 Figure 19: KM plot for INV OS (POLARIX; data cut: June 2021) ............................. 70 Figure 20: Visual fit of OS distributions to POLARIX data - Pola+R-CHP (informed by PFS) ......................................................................................................................... 73 Figure 21: Visual fit of OS distributions to POLARIX data - R-CHOP (informed by PFS) ......................................................................................................................... 73 Figure 22: OS standard extrapolation functions – Pola+R-CHP and R-CHOP (informed by PFS) .................................................................................................... 74 Figure 23: PFS extrapolation with the cure mixture model (Generalised Gamma distribution) ............................................................................................................... 76 Figure 24: Base case for PFS and OS for the ITT population (cure mixture model) 76 Figure 25: Kaplan-Meier curves showing time-to-off treatment (TTOT) for Pola+RCHP .......................................................................................................................... 77 Figure 26: Kaplan-Meier curves showing TTOT for R-CHOP ................................... 78 Figure 27: Cost-effectiveness plane for Pola+R-CHP vs. R-CHOP ........................ 104 Figure 28: Cost-effectiveness acceptability curve for Pola+R-CHP vs R-CHOP .... 104 Figure 29: Tornado diagram, Pola+R-CHP vs R-CHOP ......................................... 106

Company evidence submission for polatuzumab vedotin in combination with R-CHP for untreated diffuse large B-cell lymphoma [ID3901]

© Roche Products Ltd. 2022. All rights reserved

Abbreviations

Company evidence submission for polatuzumab vedotin in combination with R-CHP for untreated diffuse large B-cell lymphoma [ID3901]

© Roche Products Ltd. 2022. All rights reserved

Company evidence submission for polatuzumab vedotin in combination with R-CHP for untreated diffuse large B-cell lymphoma [ID3901]

© Roche Products Ltd. 2022. All rights reserved

Company evidence submission for polatuzumab vedotin in combination with R-CHP for untreated diffuse large B-cell lymphoma [ID3901]

© Roche Products Ltd. 2022. All rights reserved

B.1 Decision problem, description of the technology and clinical care pathway

B.1.1 Decision problem

The submission covers the technology’s full marketing authorisation for this indication.

Company evidence submission for polatuzumab vedotin in combination with R-CHP for untreated diffuse large B-cell lymphoma [ID3901]

© Roche Products Ltd. 2022. All rights reserved

Table 1: The decision problem

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B.1.2 Description of the technology being appraised

The technology being appraised is described below in Table 2. See Appendix C for details of the draft summary of product characteristics (SmPC) and European Public Assessment Report (EPAR).

Table 2: Technology being appraised

B.1.3 Health condition and position of the technology in the treatment pathway

B.1.3.1 Disease overview

Incidence, prevalence and mortality

Non-Hodgkin lymphoma (NHL) is a heterogeneous group of disorders characterised by malignant proliferation of lymphoid cells, originating from B- and T-lymphocytes. In 2020, 544,352 new NHL cases were estimated worldwide (8). In the UK, 14,200 new NHL cases are diagnosed every year, accounting for 4% of all new cancer cases (9).

NHL is divided between high and low grade NHL subtypes (8). Diffuse large B-cell lymphoma (DLBCL) is a high grade B-cell NHL with an aggressive clinical course. DLBCL is the most common type of NHL and accounts for up to 40% of all newly diagnosed NHL cases (10). In the UK, around 4,820 people are diagnosed with DLBCL each year (11). The 10-year prevalence is estimated at 27,790 cases (11). As with other types of NHL, the incidence of DLBCL increases with age and most patients are diagnosed between ages 65–74. If left untreated, patients with DLBCL have a median survival of less than a year (12).

Histophysiology and genomics

DLBCL has distinct morphologic, immunophenotypic, and genetic features. Morphologically, DLBCL is characterised by complete or partial effacement of the nodal architecture by sheets of large atypical lymphoid cells. Immunophenotypically, the disease is characterised by the expression of pan B-cell antigens (CD19, CD20, CD22, CD79a, and CD79b), as well as surface and/or cytoplasmic immunoglobulin expression (IgA and IgM) (2, 13).

A significant proportion of DLBCL remains biologically heterogeneous and is defined as DLBCL, not otherwise specified (DLBCL, NOS). Most cases of DLBCL, NOS are CD20 positive (CD20+). Distinct genetic features have further sub-classified DLBCL by revealing complex molecular patterns and distinct signalling mechanisms. Although there is no single somatic genetic change that defines the disease, the majority of cases have stepwise alterations in the immunoglobulin-heavy gene (14). The most frequently dysregulated

genes include BCL6 (rearrangement in 35–40%; mutation in 5′ noncoding region in 70%), BCL2 (translocation 15%, amplification 24%), and cMYC (5–15%) (15). Gene expression profiling has identified gene expression patterns that further subtype the disease into germinal centre B-cell (GCB) and activated B-cell (ABC) subgroups that have different oncogenic pathways (16).

Classification and staging

In the 2016 World Health Organization (WHO) classification of tumours of haematopoietic and lymphoid tissues, DLBCL is classified as a mature B-cell neoplasm. DLBCL has been further classified in the 2016 WHO update with multiple classification systems including cell of origin, MYC protein expression and rearrangement, and distinct somatic mutations (e.g., CREBBP/EP300, TP53, EZH2, and MYD88) (17).

The revised staging system for the classification of primary nodal lymphomas is used routinely to classify the extent of disease on the basis of the distribution and number of involved sites, as well as the presence or absence of extranodal involvement and constitutional symptoms (18).

Table 3: Ann Arbor Staging System for lymphoma

Extent of disease is determined by positron emission tomography/computed tomography (PET-CT) for avid lymphomas and CT for nonavid histologies. Tonsils, Waldeyer's ring, and spleen are considered nodal tissues. * Whether Stage II bulky disease is treated as limited or advanced disease may be determined by histology and a number of prognostic factors

Diagnosis and causes

DLBCL is diagnosed from an excisional tissue biopsy, usually of an involved lymph node or from an extranodal site. While an excisional lymph node biopsy is the preferred diagnostic test for most patients, some patients may not present with easily accessible lymphadenopathy or require the pathologic evaluation of another tissue e.g. pleural fluid, spleen, for diagnosis. Histological evaluation is performed in accordance with the WHO Classification for Lymphoid Malignancies, which categorises lymphomas based on cytology, immunophenotype, and genetic and clinical features (17). Approximately 45–60% of patients present with advancedstage disease (Ann Arbor Stage III or IV, see Table 3) at diagnosis.

Most cases of DLBCL, NOS arise de novo ; however, this diagnosis also includes progression or transformation of low-grade B-cell malignancy, such as follicular lymphoma (FL), chronic lymphocytic leukaemia (CLL) and small lymphocytic lymphoma (SLL) (19). Other DLBCL subtypes are associated with the Epstein-Barr virus (EBV) (20, 21) and the Kaposi's sarcomaassociated herpesvirus (KSHV) (22).

The causes of DLBCL are not well understood for the vast majority of patients. Several risk factors have been identified for development of the disease, including hereditary and acquired immune deficiencies, occupational exposures, and pharmacological immunosuppression in the setting of transplantation or treatment of autoimmune diseases.

Prognosis

Prognosis of patients with aggressive NHL is most commonly predicted using the International Prognostic Index (IPI). IPI is based on five risk factors obtained at diagnosis that are independent predictors for outcomes such as overall survival (OS) and progression-free survival (PFS):

• Age (≤60 vs. >60 years)

• Serum lactate dehydrogenase (LDH; normal vs. elevated) level

• Eastern Cooperative Oncology Group (ECOG) performance status (PS) (0/1 vs. 2–4)

• Ann Arbor Stage (I or II vs. III or IV)

• Number of extranodal sites (0 or 1 vs. 2–4)

Patients with higher IPI scores, combined with biologically defined higher-risk patients (including the ABC subtype, double-hit lymphoma [DHL], and double-expressor lymphoma [DEL] DLBCL), represent the subset of patients with the poorest outcomes with current therapies. On the basis of the number of negative prognostic features present at the time of diagnosis (age >60 years, Stage III/IV disease, elevated serum LDH, ECOG PS ≥2, >1 extranodal site of disease), four discrete outcome groups have been identified, with 5-year OS ranging from 26–73% (Table 4) (23).

In 2007, Sehn et al . confirmed the validity of the IPI in the rituximab era in a cohort of 365 patients treated with rituximab in combination with the CHOP regimen (cyclophosphamide, doxorubicin, vincristine, and prednisolone) (24). However, the IPI was able to distinguish only three rather than the four risk groups in the original IPI. The authors proposed a revised IPI (R-IPI) by redistributing the IPI factors into three prognostic groups: ‘very good’ (0 risk factors), ‘good’ (1–2 factors), and ‘poor’ (3–5 factors). The 4-year OS was 94%, 79%, and 55% in the three groups, respectively. Although the original IPI remains valid in the rituximab + cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) era, it now has more limited ability to predict patients who will suffer a particularly aggressive course, since even the ‘high-risk’ group has a greater than 50% 4-year OS (25).

Table 4: Treatment outcomes in DLBCL

Key: DLBCL, diffuse large B-cell lymphoma; OS, overall survival. Source: (23) 5-year OS, (25) for 3-year OS, (24) for 4-year OS.

Bulky disease has been defined as an adverse prognostic factor (18) and the ABC subtype has been shown to be associated with a more aggressive clinical course than the GCB subtype (18, 19). Finally, several individual biomarkers assessed by immunohistochemistry or gene expression profiling have been identified as having prognostic significance in DLBCL, such as TP53 mutations (26), MYC rearrangement and BCL2 expression (27), although the introduction of rituximab to standard chemotherapy seems to ameliorate the negative prognostic impact of BCL2 expression (28). ‘Double-hit’ lymphomas with dual translocations involving both MYC and BCL2, or BCL6, have a particularly aggressive clinical course and poor response to standard chemotherapy (25). These biomarkers are not routinely used in clinical practice due to cost, technical complexity, and tissue requirements and they do not currently influence treatment choices.

Quality of life

DLBCL is an aggressive, high-grade lymphoma that is fatal without treatment. Untreated DLBCL patients have an estimated life expectancy of less than one year (4). Symptom presentation in DLBCL is variable and dependent on the site of disease involvement. Patients with DLBCL typically present with a rapidly enlarging symptomatic mass, most commonly nodal enlargement in the neck or abdomen, or, in the case of primary mediastinal large B-cell lymphoma, the mediastinum, but may also present as a mass lesion anywhere in the body. The most common extranodal sites are the gastrointestinal tract, head and neck, and skin and soft tissue. Bone marrow is involved in 10–15% of cases (29). Systemic B symptoms, such as fever, unintentional weight loss, and recurrent night sweats, are observed in approximately 30% of patients, and the serum lactate dehydrogenase (LDH) is elevated in over 50% of patients.

There are limited data on the impact of first-line (1L) DLBCL on patients’ quality of life (QoL). However, studies have shown that the QoL burden was higher and more impaired in patients who did not respond well to 1L treatments, patients with an aggressive form of NHL, and in younger DLBCL patients:

• In a systematic review by Chadda et al. , DLBCL patients who achieved complete response after 1L treatment had significantly greater improvements on QoL compared to non-complete responders (30).

• A study by Alawi et al. compared QoL in patients with indolent and aggressive lymphoma. It was found that patients with the aggressive form of NHL demonstrated a somewhat lower QoL, including physical, social/family, emotional, and functional wellbeing, as well as higher anxiety and lower positive affect than patients with indolent NHL (31).

• A study by van der Poel et al. found that younger DLBCL survivors (aged 18–59 years) showed worse scores on dyspnoea, cognitive and social functioning, as well as financial problems compared to their age- and sex- matched normative population (32). This suggests that DLBCL has a greater impact on younger than older survivors.

It is evident that disease-related symptoms are frequent in most DLBCL patients. Most importantly, current treatment options are not effective in all patients (see section B.1.3.2), and subsequent therapy only offer a curative option in a minority group of patients. Patients who are refractory to or relapse following 1L treatment experience even greater anxiety due to the poorer prognosis of their condition and the need for further, often more intensive treatment. The cumulative burden of toxicity on patients’ physical and psychological health further impact their QoL negatively. This is partly related to uncertainties towards the prognosis of their disease, side effects of treatment and fear of relapse (33), especially given the disappointing efficacy of standard salvage regimens prior to transplant (34). Additionally, this will also increase the demand on hospital services and the use of skilled nursing facilities and hospice services (35).

Compared to 1L patients, advanced DLBCL patients have a higher burden of illness, including increases in admissions, emergency room visits, use of skilled nursing facilities, home health agencies, and hospice services (36). In other words, targeted 1L DLBCL treatments have the potential to reduce disease progression and significantly improve patients’ QoL, allowing for a more cost-effective, budgeted treatment paradigm in the overall management of DLBCL.

B.1.3.2 Current clinical practice in the UK

According to the British Society of Haematology (BSH) (37) and the Pan-London HaematoOncology Clinical Guidelines (38), the current 1L therapy in previously untreated DLBCL is rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP). Depending on staging, patients are treated with 3–6 cycles of R-CHOP, sometimes followed by involved site radiation treatment (ISRT). However, standard R-CHOP regimen is only effective in 50–70% of patients (39). Among patients for whom R-CHOP therapy fails, 10–15% suffer from primary refractory disease (disease does not enter complete remission and/or progresses during or soon after treatment), whereas 20–30% relapse after achieving complete remission (37). The chance for long-term cure in DLBCL diminishes with each subsequent line of therapy; therefore, obtaining the best possible outcome for previously untreated patients is of critical importance.

In the setting of 1L DLBCL, the majority of disease relapse occurs within the first 24 months after starting treatment. Recent analyses have demonstrated that patients with DLBCL who have remained in remission after this period have survival equivalent to that of age-, sex-, and country-matched general population (40). However, approximately half of the patients will not respond to subsequent therapy because of refractory disease (34). Thus, optimising the initial treatment options in settings with curative intent would have a substantial impact on the overall outcome for DLBCL.

Since the introduction of R-CHOP, there has been no advancement in treatment options for previously untreated DLBCL patients for over 20 years. While R-CHOP may cure approximately 60% of patients with previously untreated DLBCL (41), the majority of randomised studies and alternative strategies have so far been unable to demonstrate meaningful benefit over R-CHOP. These include:

• Increased dose density with R-CHOP given at 14-day intervals (42, 43);

• Dose intensification with dose-adjusted etoposide plus prednisolone, vincristine, cyclophosphamide, doxorubicin and rituximab (DA-EPOCH-R) (44);

• Substitution strategies, such as the anti-CD20 monoclonal antibody obinutuzumab (45);

• Additive agents to R-CHOP, such as bevacizumab (46), bortezomib (47), ibrutinib (48), and lenalidomide (49); and

• Maintenance strategies after R-CHOP with lenalidomide (50), everolimus (51), and enzastaurin (52).

In some of these cases, new treatment regimens may have been quite promising in the early phase studies of DLBCL; however, the feasibility of adding therapies to R-CHOP or increasing dose density resulted in higher rates of adverse events and complications or limited tolerability of the treatment. Therefore, there is a significant unmet medical need in the 1L DLBCL setting and a strong rationale for introducing novel therapeutic agents that can build upon R-CHOP and improve outcomes in patients with previously untreated DLBCL by preventing or delaying relapse.

B.1.3.3 Disease management pathway

The proposed treatment pathway and position of polatuzumab vedotin is summarised in Figure 1. The information presented below is based on NICE, BSH, and National Health Service (NHS) guidelines for the diagnosis and management of DLBCL (37, 38).

Figure 1: Current treatment pathway for adult patients (aged 18-80) with previously untreated DLBCL (including Pola+RCHP positioning)

==> picture [631 x 301] intentionally omitted <==

The grey box indicates the proposed positioning of Pola+R-CHP for patients with an IPI of 2–5. Key: IPI, International Prognostic Index; ISRT, involved site radiotherapy.

B.1.4 Equality considerations

The technology is not likely to raise any equality issues.

B.2 Clinical effectiveness

B.2.1 Identification and selection of relevant studies

See Appendix D for full details of the process and methods used to identify and select the clinical evidence relevant to the technology being appraised.

B.2.2 List of relevant clinical effectiveness evidence

Table 5: Clinical effectiveness evidence

B.2.3 Summary of methodology of the relevant clinical effectiveness evidence

Unless otherwise stated, B.2.3–B.2.6 is based on the POLARIX (study GO39942) clinical study report (CSR) (data on file).

B.2.3.1 Study methodology

Study design

POLARIX (study GO39942) is a Phase III, multicentre, randomised, double-blind, placebocontrolled trial comparing the efficacy and safety of polatuzumab vedotin in combination with rituximab and CHP (R-CHP) versus rituximab and CHOP (R-CHOP) in previously untreated patients with diffuse large B-cell lymphoma. The study schema is shown in Figure 2.

Figure 2: Overview of the design of POLARIX (study GO39942) (53)

==> picture [453 x 185] intentionally omitted <==

*Western Europe, United States, Canada and Australia vs Asia vs Rest of World.

Key: BICR, blinded independent central review; CR, complete response; ECOG PS, Eastern Cooperative Oncology Group performance status; EFSefficacy, event-free survival for efficacy causes (time from randomization to the earliest occurrence of disease progression/relapse, death due to any cause, initiation of any non-protocol specified anti-lymphoma treatment, or biopsy-confirmed residual disease after treatment completion); EOT, end of treatment; INV, investigator; IPI, International Prognostic Index; LYSA, Lymphoma Study Association; PET, positron emission tomography; Q21D, every 21 days; R, randomization; R-CHP, rituximab plus cyclophosphamide, doxorubicin, prednisolone; R-CHOP, rituximab plus cyclophosphamide, doxorubicin, vincristine, prednisolone.

Inclusion/exclusion criteria

Patients with previously untreated CD20-positive DLBCL were enrolled in POLARIX. See Appendix E for a full list of inclusion/exclusion criteria.

Randomisation and cycles of treatment

A total of 879 patients were randomised in a 1:1 ratio to either Arm A or Arm B as defined below. Both patients and the investigator were blinded to the assigned active microtubule inhibitor (i.e., polatuzumab vedotin or vincristine) and placebo control. Patients received six cycles of either Pola+R-CHP or R-CHOP chemotherapy at 21-day intervals. Both arms then received two additional cycles of single agent rituximab.

• Arm A, Pola+R-CHP (investigational arm): polatuzumab vedotin 1.8 mg/kg IV, placebo for vincristine IV, rituximab 375 mg/m[2] IV, cyclophosphamide 750 mg/m[2] IV, and doxorubicin 50 mg/m[2] IV each given on Day 1 and prednisolone 100 mg/day orally (PO) given on Days 1–5 of every 21-day cycle for 6 cycles. Rituximab 375 mg/m[2] IV was given as monotherapy in Cycles 7 and 8.

• Arm B, R-CHOP (control arm) : placebo for polatuzumab vedotin, rituximab 375 mg/m[2] IV, cyclophosphamide 750 mg/m[2 ] IV, doxorubicin 50 mg/m[2] IV, and vincristine 1.4 mg/m[2] IV (maximum 2 mg/dose) each given on Day 1 and prednisolone 100 mg/day PO given on Days 1–5 of every 21-day cycle for 6 cycles. Rituximab 375 mg/m[2] IV was given as monotherapy in Cycles 7 and 8.

POLARIX was stratified to ensure there was an equal spread of patients. Patients were randomised using the following stratification factors:

• International Prognostic Index IPI score (IPI 2 versus IPI 3–5)

• Bulky disease, defined as one lesion ≥ 7.5 cm (present versus absent)

• Geographical region (Western Europe, United States, Canada, and Australia versus Asia versus Rest of World [remaining countries])

No crossover to the investigational arm was allowed. Patients were assessed for disease response by the investigator using regular clinical and laboratory examinations, dedicated computed tomography (CT) or magnetic resonance imaging (MRI) scans, and fluorodeoxyglucose positron emission tomography (FDG-PET; hereafter referred to as PETCT) according to the Lugano Response Criteria for Malignant Lymphoma.

Assessment

Response was evaluated at the end of study treatment, or sooner in the event that a patient discontinued early. After completion of therapy, all patients were followed-up at clinic visits conducted every 3 months for 24 months, and then every 6 months until Month 60. After 5 years, patients were followed only for survival and initiation of a new anti-lymphoma therapy

(NALT) approximately every 6 months until study termination, patient withdrawal of consent or death.

Safety was evaluated by monitoring all adverse events (AEs), serious adverse events (SAEs), and abnormalities identified through physical examinations, vital signs, and laboratory assessments. Such events were graded using the National Cancer Institute Common Terminology Criteria for Adverse Events, Version 4.0 (NCI CTCAE v4.0). Laboratory safety assessments included routine monitoring of haematology and blood chemistry, and tests of immunologic parameters.

B.2.3.2 Endpoints and assessments

POLARIX evaluated the efficacy, safety, pharmacokinetics, and PROs of pola plus chemoimmunotherapy (Pola+R-CHP) compared with SoC chemoimmunotherapy (R-CHOP) in previously untreated patients with CD20-positive DLBCL.

Primary efficacy endpoint

The primary study endpoint was PFS as assessed by the investigator. PFS was defined as the time from randomisation to the first occurrence of disease progression or relapse as assessed by the investigator, using the Lugano Response Criteria for Malignant Lymphoma, or death from any cause, whichever occurs earlier.

Secondary efficacy endpoints

Key secondary endpoints included in the hierarchical testing procedure:

• Event-free survival (EFSeff) as determined by the investigator

• Complete response (CR) rate at end of treatment by FDG-PET as determined by blinded independent central review (BICR)

• Overall survival (OS)

Additional secondary endpoints that were not adjusted for testing multiplicity:

• Disease-free survival (DFS)

• Best overall response (BOR) rate as determined by investigator

• Duration of response (DOR)

Exploratory endpoints

• Subgroup analysis

• Patient-reported outcomes (PROs) endpoints

• All scales of the EORTC QLQ-C30, the FACT-Lym LymS, and FACT/GOG-NTX peripheral neuropathy

B.2.3.3 Patient demographics and baseline characteristics

Key demographic and baseline disease characteristics from the primary analysis (CCOD 28 June 2021) are provided in Table 6.

The stratification was not designed to show statistical significance between the different factors. The proportions of patients in each category by IPI score, bulky disease, and geographical region were similar in the two treatment arms and there were no imbalances in stratification factors between the two arms. Treatment arms were balanced with respect to baseline biomarker assessments performed centrally.

Table 6: Summary of key demographic data and disease characteristics at baseline

a This patient with IPI "1" per eCRF is a data entry error, and was randomised as IPI "2". b Based on local diagnosis. c Epstein Barr virus-positive (EBV+) DLBCL and T-cell/histiocyte rich large B-cell lymphoma (LBCL). d Based on central review, and percentages are based on biomarker evaluable population (i.e., by excluding patients with unknown status).

Key: ECOG PS, Eastern Cooperative Oncology Group Performance Status; eCRF, electronic case report form; IPI, International Prognostic Index; LDH, lactate dehydrogenase; COO, cell of origin; DLBCL, diffuse large B-cell lymphoma; NOS, not otherwise specified; HGBL, high-grade B-cell lymphoma; ABC, activated B-cell; GCB, germinal centre B-cell; DHL/THL, double hit lymphoma/triple-hit lymphoma.

Patient disposition is summarised in Table 7. At the CCOD for the primary analysis (28 June 2021), patients had median duration of survival follow-up of 28.1 months in the Pola+R-CHP arm and 28.2 months in the R-CHOP arm.

Table 7: Patient disposition – intention-to-treat (ITT) population

Key: R-CHOP, rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone; R-CHP, rituximab plus cyclophosphamide, doxorubicin, and prednisolone.

B.2.4 Statistical analysis and definition of study groups in the relevant clinical effectiveness evidence

See Appendix D.2 for details of the number of eligible participants and patient disposition for the POLARIX trial.

Determination of sample size

In total, enrolment of 875 patients was planned and expected to complete in approximately 23 months, leading to an average monthly recruitment of 38 patients per month.

The sample size considerations were based on the following assumptions:

• 1:1 randomisation ratio in Pola+R-CHP versus R-CHOP

• A one-sided log-rank test

• 80% power at the 2.5% significance level

• A 31% reduction in the risk of disease progression, relapse, or death (i.e. PFS hazard ratio of 0.69).

• PFS in the control arm was assumed to follow a piece-wise exponential distribution, and the hazard rate over time h(t) is estimated using the historical data obtained from the GOYA study among patients with IPI 2–5 who received R-CHOP (data on file).

Based on these assumptions, approximately 228 investigator-assessed PFS events were needed to detect a hazard ratio of 0.69 in PFS (3-year PFS rates of 62–72%), with 80% power for the primary analysis of PFS. The minimal detectable difference (MDD) for PFS hazard ratio at the final PFS analysis was 0.771 (i.e. 22.9% reduction in the risk of disease progression, relapse, or death). 3-year PFS was expected to improve from 62–70% under the MDD.

Efficacy analyses

The analysis population for the primary and secondary efficacy analyses consisted of all randomised patients, with patients grouped according to their assigned treatment.

To control the overall type I error rate at a one-sided 0.025 level of significance, a hierarchical testing procedure, including possible  recycling (54), was used to adjust for multiple statistical testing of the primary and key secondary efficacy endpoints. The POLARIX statistical analysis plan is summarised below (Figure 3).

Figure 3: POLARIX statistical analysis plan

==> picture [424 x 330] intentionally omitted <==

1. First, the primary efficacy endpoint, PFS by investigator in the ITT population, was tested at =0.025.

2. If the primary PFS endpoint was statistically positive, a formal statistical test of EFSeff by investigator in the ITT population between the two arms was performed at a one-sided =0.025 using a stratified log-rank test.

3. If the secondary EFSeff endpoint was statistically positive, the EOT CR rate by IRC in the ITT population was tested using a stratified Cochran-Mantel-Haenszel (CMH) test at a onesided =0.005.

4. If the EOT CR rate by BICR in was statistically significant, the final OS analysis was tested at a one-sided =0.025; otherwise, the final OS was tested at a one-sided =0.02.

B.2.5 Quality assessment of the relevant clinical effectiveness evidence

The quality assessment of the POLARIX trial is shown below ( Table 8 ). See Appendix D.3 for the complete quality assessment of other relevant trials.

Table 8: Risk of bias assessment for POLARIX

B.2.6 Clinical effectiveness results of the relevant trials

The key efficacy findings from the POLARIX trial are summarised below, which provides an overview of the efficacy in patients with previously untreated DLBCL.

B.2.6.1 Primary efficacy endpoint: progression-free survival (PFS)

A statistically significant and clinically meaningful improvement in the primary endpoint of Investigator-assessed PFS was demonstrated following treatment with Pola+R-CHP compared to R-CHOP, in patients with previously untreated DLBCL (Table 9).

Table 9: Summary of investigator-assessed PFS (ITT population)

*Stratified for IPI score (IPI 2 vs IPI 3–5), bulky disease (present vs absent), and geographical region (Western Europe, United States, Canada and Australia versus Asia versus Rest of World [remaining countries]). †Kaplan–Meier estimate. Key: CI, confidence interval.

Fewer patients in the Pola+R-CHP arm had progressed or died compared to the R-CHOP arm (107 [24.3%] vs.134 [30.5%]). Treatment of patients with previously untreated DLBCL with the Pola+R-CHP regimen resulted in a statistically significant and clinically meaningful reduction in the risk of progression, relapse or death by 27% compared with patients treated with the R-CHOP regimen (stratified HR: 0.73 [95% CI: 0.57–0.95]; p=0.0177). Results of the unstratified analysis were consistent with the results of the stratified analysis (HR: 0.76 [95% CI: 0.59–0.98]; p=0.0326).

The separation in the KM curves at approximately 6 months of observation coincides with treatment completion assessments (Figure 4). The majority of PFS events occurred within 24 months of randomisation in both arms, with a higher proportion of patients remaining alive and progression-free in the Pola+R-CHP arm compared to the R-CHOP arm at 12-Month (83.9% [95% CI: 80.43–87.39] vs 79.8% [95% CI: 75.92–83.61]), and 24-Month (76.7% [95% CI:72.65–80.76] vs 70.2% [95% CI: 65.80–74.61]). The observed treatment difference

in PFS event-free rate increased from the 12-Month milestone (4.14 [95% CI: -1.05–9.32]) to the 24-Month milestone (6.5 [95% CI: 0.52–12.49]).

Figure 4: Kaplan-Meier plot of time to investigator-assessed PFS (ITT population) (53)

==> picture [476 x 271] intentionally omitted <==

B.2.6.2 Secondary efficacy endpoints

Key secondary endpoints included in the hierarchical testing procedure are as follows:

• EFSeff as determined by the investigator

• CR rate at end of treatment by FDG-PET as determined by BICR

• OS

Key findings from the secondary endpoints are summarised below in Table 10.

Table 10: Key secondary efficacy endpoint results for pola (ITT population)

1 Summaries of EFSeff by Investigator (median, percentiles) are Kaplan-Meier estimates. 95% Cl for median was computed using the method of Brookmeyer and Crowley. Hazard ratios were estimated by Cox regression.

2 95% Cl for rate were constructed using the Clopper-Pearson method. 95% Cl for difference in response rates were constructed using Wilson method.

3 Summaries of OS (median, percentiles) are Kaplan-Meier estimates. 95% Cl for median was computed using the method of Brookmeyer and Crowley. Hazard ratios were estimated by Cox regression.

Key: CR, complete response; BICR, blinded independent central review; CMH, Cochran-MantelHaensze; EFSeff, event-free survival for efficacy reasons; HR, hazard ratio; INV, investigator; pola, polatuzumab vedotin; ITT, intent-to-treat; PET-CT, positron emission tomography-computed tomography; R-CHOP, rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone; R-CHP, rituximab plus cyclophosphamide, doxorubicin, and prednisolone.

Investigator-assessed event-free survival (EFSeff)

At the time of the CCOD, 112 patients (25.5%) in the Pola+R-CHP arm, and 138 patients (31.4%) in the R-CHOP arm had an EFS event. Results of the secondary endpoint EFSeff were statistically significant, highly consistent with results of the primary endpoint of investigator-assessed PFS, and supportive of the clinical benefit for Pola+R-CHP compared with R-CHOP. A statistically significant reduction by 25% in the risk of an EFSeff event was observed in the Pola+R-CHP arm compared with the R-CHOP arm (stratified HR: 0.75 [95% CI: 0.58–0.96], p=0.0244). The KM curves for EFSeff began to separate at approximately 6 months after randomisation in favour of Pola+R-CHP and the separation was maintained for the duration of follow-up (Figure 5).

Figure 5: Kaplan-Meier plot of investigator-assessed EFSeff (ITT population) (53)

==> picture [374 x 195] intentionally omitted <==

Subsequent lymphoma therapy was collected as new anti-lymphoma therapy (NALT). Amongst the patients who received NALT, the majority received it after a PFS event. Therefore, EFSeff includes an additional definition of an event as NALT due to efficacy or biopsy-positive lymphoma after study treatment in the absence of a PFS event, which accounts for important clinical considerations in DLBCL where biopsy documented residual disease or additional planned therapies represent undesired efficacy outcomes from treatment.

BICR-assessed complete response rate at end of treatment (by PET-CT)

At the end of the treatment, BICR-assessed CR rate was high in both arms. A numerically higher proportion of patients treated with Pola+R-CHP had complete response at the end of – treatment compared to patients treated with R-CHOP (78.0% [95% CI: 73.79 81.74] vs. – – 74.0% [95% CI: 69.66 78.07]). The treatment difference was 3.9% (95% CI: -1.9 9.7) and was not statistically significant (p=0.1557).

Overall survival (OS)

The frequency of OS events (deaths) were low in both arms. A total of 53 deaths (12.0% patients) were reported in the Pola+R-CHP arm, and 57 deaths (13.0% patients) were reported in the R-CHOP arm. With very few events in both arms, OS results were still immature at the time of the interim analysis of OS and did not meet the pre-specified – threshold for statistical significance (stratified HR: 0.94 [95% CI: 0.65 1.37]; p=0.7524). The unstratified analysis of OS showed results similar to the stratified analysis. A KM curve is shown below in Figure 6. Milestone OS results for the Pola+R-CHP arm and the R-CHOP arm were 92.2% and 94.6% at 12-Month, and 88.7% and 88.6% at 24-Month, respectively.

Figure 6: Kaplan-Meier plot of time to overall survival (ITT population) (53)

==> picture [347 x 280] intentionally omitted <==

New anti-lymphoma treatment (NALT)

NALT could be administered after the patient had completed study treatment, and included both radiotherapy or systemically administered therapies. NALT was allowed to be administered with or without a disease progression documented in the patient.

The total number of patients with at least one NALT was higher in the R-CHOP arm (30.3%) compared to the Pola+R-CHP arm (22.5%), consistent with the higher number of patients who had a PFS event (Table 11). Similarly, the total number of NALT treatments administered was higher in the R-CHOP arm (290) compared to the Pola+R-CHP arm (179). The number of patients each receiving radiotherapy, systemic therapy, stem cell transplants and chimeric antigen receptors cell therapy (CAR-T) were also higher in the R-CHOP arm compared to the Pola+R-CHP arm (see Table 11).

The percentage of patients receiving radiotherapy (pre-planned or unplanned) was lower in the Pola+R-CHP group than in the R-CHOP group (9.3% vs. 13.0%), as was the percentage of patients receiving systemic therapy (17.0% vs. 23.5%), including stem-cell transplantation

(3.9% vs. 7.1%) and chimeric antigen receptor (CAR) T-cell therapy (2.0% vs. 3.6%). After disease progression, unblinding was permitted for individual patients, and 8 patients (all in the R-CHOP group) received pola as part of a subsequent therapy.

Table 11: Follow-up anti-lymphoma treatments (ITT population)

*Subsequent anti-lymphoma treatment is defined as non-protocol anti-lymphoma therapy (NALT), and does not include intrathecal central nervous system disease prophylaxis as part of treatment; pre-planned radiotherapy is included within radiotherapy here, though is not included as an event in EFS analyses; †Includes any monotherapy, multi-drug, or cell-based regimen. Key: CAR, chimeric antigen receptor; EFS, event-free survival; ITT, intention-to-treat.

Additional secondary efficacy endpoints (not formally tested)

The results of additional (non -controlled) secondary endpoints further support pola’s positive treatment effect observed on investigator-assessed PFS (Table 12).

Table 12: Other selected secondary endpoints (not formally tested)

1 Summaries of EFSall/DFS/DOR by investigator (median, percentiles) were Kaplan-Meier estimates. 95% Cl for median was computed using the method of Brookmeyer and Crowley. Hazard ratios were estimated by Cox regression.

2 95% Cl for rate were constructed using the Clopper-Pearson method. 95% Cl for difference in response rates were constructed using Wilson method.

Key: BOR, best overall response; DFS, disease-free survival; DOR, duration of response.

Investigator-assessed duration of response (DOR) and best overall response (BOR)

In patients who achieve partial or complete response (PR or CR), DOR favoured Pola+RCHP compared to R-CHOP (stratified HR 0.74 [95% CI: 0.56–0.98]). Treatment with Pola+R-CHP reduced the risk of progression or death in patients with a CR or PR by 26% compared to patients with a CR or PR who received treatment with R-CHOP. BOR showed high response rates (i.e. best response of CR or PR while on study) in both the Pola+R-CHP arm xxxxxxxxxxxxxxxxxxxxxxxxxxxxx and the R-CHOP arm

xxxxxxxxxxxxxxxxxxxxxxxxxxxxx. Treatment difference was xxxxxxxxxxxxxxxxxxxxxxxxxx in favour of Pola+R-CHP.

Investigator-assessed disease-free survival (DFS)

In patients who achieve CR, the favourability of the Pola+R-CHP arm compared to the R- CHOP arm in DFS suggests that even though CR was high in both treatment arms, remission status was more durable in the Pola+R-CHP arm; treatment with Pola+R-CHP reduced the risk of progression or death by 30% compared to treatment with R-CHOP (stratified HR 0.70 [95% CI: 0.50–0.98]).

B.2.7 Subgroup analysis

POLARIX was not designed or powered to compare patient subgroups. The univariate INVPFS subgroup analyses were exploratory, unstratified, pre-planned, and signal seeking. There was a directionally consistent treatment effect supporting the PFS benefit of Pola+RCHP in the majority of subgroups (HR<1), including patient demographic and patient characteristics, local histopathologic diagnosis, and centrally tested biologic subgroups (Table 13). Additionally, all subgroups included a confidence interval that favoured Pola+RCHP. However, event numbers and sample size were limited, and the balance of patient baseline characteristics in the study arms might not be retained in the subgroups.

Given the known limitations of the exploratory subgroup analyses, results should not be over-interpreted and there was no statistical evidence for heterogeneity of treatment effect in any of the subgroups. Further biomarker analyses are ongoing to better understand the observed differences in the study population.

Table 13: Investigator-assessed PFS by subgroup (unstratified)

Key: ECOG PS, Eastern Cooperative Oncology Group - Scale of Performance Status; IHC, immunohistochemistry; IPI, International Prognostic Index; LDH, lactate dehydrogenase.

B.2.8 Meta-analysis

No meta-analysis was conducted for this submission.

B.2.9 Indirect and mixed treatment comparisons

No indirect and mixed treatment comparisons were conducted for this submission.

B.2.10 Adverse reactions

B.2.10.1 Overview of safety

Out of the 879 patients randomised in the study, 873 received treatment (435 in the Pola+RCHP arm; 438 in the R-CHOP arm) and were included in the safety-evaluable population.

Pola in combination with R-CHP in patients with previously untreated DLBCL was generally well tolerated and toxicities were manageable. The safety profile of the Pola+R-CHP regimen was comparable to R-CHOP and in line with the known safety profiles of each individual component and the underlying disease. A comparable incidence of AEs leading to any treatment discontinuations between the treatment arms was noted and a lower incidence of AEs leading to any dose reductions in the Pola+R-CHP arm driven by fewer dose reductions due to peripheral neuropathy (PN) was noted, demonstrating that tolerability of Pola+R-CHP was comparable and descriptively better than that of R-CHOP. No new safety signals were identified.

Overall safety findings are summarised below and a summary of key AE-related safety is presented in Table 14:

• The incidence of AEs of any grade in patients in the Pola+R-CHP arm (97.9%) was comparable with the R-CHOP arm (98.4%).

• The most common AEs (AEs affecting over 50% of patients) by System Organ Class (SOC) in either treatment arm (with percentages expressed as Pola+R-CHP vs. R- CHOP) were:

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.

• The most common AEs by Preferred Term (PT) (reported by at least 20% of patients in either arm, with percentages expressed as Pola+R-CHP vs. R-CHOP) were: nausea (41.6% vs 36.8%), neutropenia (30.8% vs 32.6%), constipation (28.7% vs

29.0%), anaemia (28.7% vs 26.0%), fatigue (25.7% vs 26.5%), diarrhoea (30.8% vs 20.1%), alopecia (24.4% vs 24.0%), PN (24.1% vs 22.6%), and peripheral sensory neuropathy (19.5% vs 21.5%).

• xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.

• The incidence of Grade 5 AEs was comparable between Pola+R-CHP (3.0%) and R- CHOP (2.3%). Most of the Grade 5 AEs in both arms were due to infections or complications of infection.

• The incidence of SAEs was comparable between Pola+R-CHP (34.0%) and R- CHOP (30.6%). The most common SAE in the Pola+R-CHP and R-CHOP arms (≥5% of patients in either arm) was xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.

• The proportion of patients who experienced AEs leading to discontinuation of any study treatment in the Pola+R-CHP arm (6.2%) was comparable to the R-CHOP arm (6.6%).

• The proportion of patients who experienced AEs leading to discontinuation of pola study treatment in the Pola+R-CHP arm (4.4%) was comparable to the proportion of patients who experienced AEs leading to discontinuation of vincristine study treatment in the R-CHOP arm (5.0%).

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx This difference was primarily

driven by a higher incidence of AEs related to PN in the R-CHOP arm.

• The proportion of patients who experienced AEs leading to any study treatment dose reduction was lower in the Pola+R-CHP arm (9.2%) compared to the R-CHOP arm (13.0%).

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

xx

Table 14: POLARIX safety profile (safety-evaluable population)

Key: AE, adverse event; SAE, serious adverse event.

Exposure to study treatment

Overall, the majority of patients treated with Pola+R-CHP and R-CHOP received their planned doses of chemotherapy.

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxx

For the investigational agents that were administered in a blinded fashion, a higher number of patients received all six planned doses of pola in the Pola+R-CHP arm (91.7% [435 patients]) compared to the number of patients who received all six planned doses of vincristine in the R-CHOP arm (88.5% [436 patients]).

Patients in the Pola+R-CHP arm received a median of 6 cycles of pola (range 1-6), corresponding to a median treatment duration of xxxxxxxxxx. A total of 91.7% (399 patients) received 6 cycles of pola treatment.

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Patients in the R-CHOP arm received a median of 6 cycles of vincristine (range 1–6), corresponding to a median treatment duration of xxxxxxxxxx. A total of 88.5% (386 patients) received 6 cycles of vincristine treatment.

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

xxxxx

B.2.10.2 Adverse events (AEs)

The proportion of patients with at least one AE in the pola+R-CHP arm (97.9% [426 patients]) was comparable to the R-CHOP arm (98.4% [431 patients]).

The most common AEs (≥ 50% of patients in either arm) by SOC were (Pola+R-CHP arm and R-CHOP arm, respectively):

• xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx • xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx • xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxx • xxxxxxxxxxxxxxxxxxxxx • xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxx

The most commonages (≥10% of patients in either arm) by PT are shown in Table 15.

AEs by PT with a difference (≥5%) in incidence between the Pola+R-CHP and R-CHOP arms were diarrhea (30.8% vs. 20.1%) and febrile neutropenia (14.3% vs. 8.0%). All other AEs occurred with a less than 5% difference in incidence between treatment arms.

Table 15: Summary of AEs with an incidence rate of at least 10% by preferred term (safety-evaluable population)

Percentages are based on ‘n’ in the column headings. For frequency counts by preferred term, multiple occurrences of the same AE in an individual are counted only once. Includes treatment-emergent AEs during AE reporting period only, which is defined as new or worsening AE from the first dose of any study drug through 90 days after the last dose of any study drug or prior to NALT, whichever is earlier.

Serious adverse events (SAEs)

The proportion of patients with at least one SAE in the Pola+R-CHP arm (34.0% [148

patients]) was comparable with the R-CHOP arm (30.6% [134 patients]).

SAEs were most commonly reported (≥5% of patients in either arm) in the following SOCs (Pola+R-CHP arm and R-CHOP arm, respectively):

A summary of the most common SAEs (≥1% of patients in either arm) by PT is shown in

Table 16. The most common PTs are contained within the most commonly reported SOCs.

The only SAEs by PT with differences (≥1% between the arms) were (Pola+R-CHP arm and R-CHOP arm, respectively):

No patients in either treatment arm discontinued study treatment due to the SAEs of febrile neutropenia, diarrhea or urinary tract infection.

Table 16: Summary of SAEs with an incidence rate of at least 1% (safety-evaluable

population)

Percentages are based on n in the column headings. For frequency counts by preferred term, multiple occurrences of the same AE in an individual are counted only once. Includes treatment-emergent AEs during AE reporting period only, which is defined as new or worsening AE from the first dose of any study drug through 90 days after the last dose of any study drug or prior to NALT, whichever is earlier.

Adverse events of particular interest (AEPIs)

AEPIs are identified or potential risks of pola for which additional analyses are presented to support the benefit-risk assessment. AEPIs were generally comparable between the Pola+R-CHP and R-CHOP arms, with some numerical differences in certain AEPI categories but the overall safety profile was consistent with the known safety profile of each drug as well as the underlying disease.

Neutropenia, including febrile neutropenia

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

xxx No Grade 5 neutropenia events were reported.

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx x xxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Neutropenia is an identified risk and an adverse drug reaction of pola, which partially explains the higher incidence of febrile neutropenia in the Pola+R-CHP arm. The relatively lower incidence of prophylactic granulocyte colony-stimulating factor (G-CSF) use in the Pola+R-CHP (90.1%) arm compared to the R-CHOP (93.2%) arm may partially contribute to the higher incidence of febrile neutropenia in Pola+R-CHP arm. The higher incidence of

febrile neutropenia in the Pola+R-CHP arm did not lead to an increase in study treatment discontinuations, dose reductions or study treatment interruptions compared with the R- CHOP arm.

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Peripheral neuropathy (PN)

The overall incidence of PN was comparable between the Pola+R-CHP and R-CHOP arms (52.9% vs 53.9%).

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

A later time to onset of PN events in the Pola+R-CHP arm compared with the R-CHOP arm, in combination with a similar median time to PN resolution between the treatment arms, likely contributed to more patients with unresolved PN events in the Pola+R-CHP arm at the time of CCOD. This is consistent with the observations from the PRO data on PN, which descriptively suggest later time to onset and lower severity of PN events in the Pola+R-CHP arm compared with the R-CHOP arm.

Anaemia

The incidence, seriousness and reversibility of anaemia events were comparable between the Pola+R-CHP and R-CHOP arms. The majority of anaemia events were Grade 1 or 2. A slightly higher incidence of Grade 3–4 anaemia was observed in the Pola+R-CHP arm compared to R-CHOP arm (12.0% vs 8.4%), however this did not lead to an increase in study treatment discontinuation, dose reduction or treatment interruptions compared with the R-CHOP arm. The majority of the anaemia events were reported as resolved as of the clinical cut-off date.

Deaths

At the time of CCOD on 28 June 2021,

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Safety conclusion

Overall, the Pola+R-CHP regimen in patients with previously untreated DLBCL achieved a safety profile consistent with the known risks of each individual component and the underlying disease, and no new safety signals were identified. The safety profile of the Pola+R-CHP combination was comparable to that of R-CHOP. Numerically fewer patients experienced dose reductions due to AEs in the Pola+R-CHP arm compared to the R-CHOP arm and were driven by fewer dose reductions due to PN. The data in this double-blinded study suggests that the safety and tolerability of Pola+R-CHP was comparable to that of R- CHOP, a regimen that is generally well-tolerated and typically administered in the outpatient setting.

B.2.11 Ongoing studies

The POLARIX study design stipulated that all patients be followed for at least 24 months after treatment initiation, which covers the period when most of the disease relapses occurred, thus ensuring that the observed treatment benefits of pola were reliable, particularly with respect to the primary endpoint of PFS. After completion of therapy, all patients were followed at clinic visits conducted every 3 months for 24 months, and then every 6 months until Month 60. Final analyses are planned for PFS in the ITT population and OS (which were immature at the time of the interim analysis).

B.2.12 Innovation

Mode of action

Polatuzumab vedotin (pola) is an anti-CD79b antibody-drug conjugate (ADC). ADCs are an innovative class of anticancer treatment agents that comprise a monoclonal antibody targeted to a tumour antigen, a chemical linker, and a potent cytotoxic agent, which is often too toxic to be given as conventional chemotherapy (55). The characteristics of the linker component of an ADC are key to ensuring that the ADC molecule remains relatively stable in the circulation to prevent non-specific release of the cytotoxic agent, yet allowing the linker to be cleaved to release the cytotoxin within a specific microenvironment within the tumour cell (56). Improvements to linker technology associated with highly potent cytotoxic payloads have permitted the development of targeted ADCs that offer meaningful efficacy while minimising side effects (57).

Figure 7: Polatuzumab vedotin molecule

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The pola molecule consists of a potent anti-mitotic agent (monomethyl auristatin E [MMAE]) covalently attached to a CD79b directed humanised IgG1 monoclonal antibody through a protease cleavable linker, maleimidocaproyl valine citrulline p aminobenzyloxycarbonyl (7) (Figure 7). CD79b is a signalling component of the B cell receptor expressed on the surface

of B cells and is found in abundance in people with DLBCL. Pola is the only ADC targeting CD79b, which allows it to preferentially deliver MMAE to B cells, resulting in anti-cancer activity against B cell malignancies. As such, CD79b expression is restricted to normal cells within the B cell lineage (with the exception of plasma cells) and malignant B-cells; therefore, targeted delivery of MMAE is expected to be restricted to these cells (Figure 8). After internalisation, the conjugate is cleaved by lysosomal enzymes to release MMAE, which binds to tubulin, disrupts the microtubule network, and results in the inhibition of cell division and cell growth, and induction of apoptosis (2, 6). MMAE has a mechanism of action that is similar to vincristine, a cytotoxic agent used in DLBCL therapy.

Figure 8: Mechanism of action of antibody-drug conjugates

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Unmet need

Since the introduction of R-CHOP, there has been no advancement in treatment options for previously untreated DLBCL patients for over 20 years. Although R-CHOP is the standard of care for patients with previously untreated DLBCL, approximately 30–50% of patients are not cured by this treatment, depending on the stage of disease and prognostic index (58) (see Section B.1.3.2). Patients who achieved a complete response (CR) after 1L treatment have demonstrated significant improvements in QoL compared to non-complete responders (30). However, approximately half of the patients will not respond to subsequent therapy because of refractory disease (34), and a significant number are ineligible for this intensive therapy because of age, comorbidities or chemotherapy-insensitive disease (34, 59).

Following 1L treatment failure, patients experience even greater anxiety due to the poorer prognosis of their condition and the need for further, often more intensive treatment as they enter the refractory/relapse space. Patients with high grade NHL demonstrated a lower QoL compared to patients with low grade NHL, including physical, social/family, and emotional factors, functional well-being, as well as higher anxiety (31). This is partly related to uncertainties towards the prognosis of their disease, side effects of treatment and fear of relapse (33), especially given the disappointing efficacy of standard salvage regimens prior to transplant (34). This will also increase the demand on hospital services and the use of skilled nursing facilities and hospice services (35).

Additionally, the use of rituximab-containing 1L therapy may be making it more difficult to salvage patients who have entered the relapse/refractory space. The CORAL study demonstrated that among patients who had failed initial induction, those who had been exposed to rituximab were 28% less likely to proceed to second randomisation (60). The 3- year EFS was merely 21% in patients who were refractory or had experienced relapse <1 year after R-CHOP. The ORCHARRD study attempted to explore the potential of ofatumumab, an anti-CD20 monoclonal mAb that targets a different epitope to rituximab, though no difference in efficacy was found between ofatumumab and rituximab as salvage treatment for relapsed/refractory DLBCL (61). The chance for long-term cure in DLBCL diminishes with each subsequent line of therapy; therefore, obtaining the best possible outcome for previously untreated patients is of critical importance.

POLARIX is the first trial in over 20 years to show a meaningful improvement in the benefitrisk profile over R-CHOP in an international Phase III double-blind, randomised controlled trial. In the randomised phase of the study, Pola+R-CHP clearly demonstrated a statistically significant and clinically meaningful INV-PFS improvement over R-CHOP in the ITT population, inclusive of high-risk subpopulations with poor prognostic factors. The observed HR of 0.73 represents a 27% decrease in relative risk of disease progression, relapse, or death; or, in other words, Pola+R-CHP spares approximately 1 out of 4 patients who would otherwise have a PFS event with R-CHOP from having that event. The avoidance of PFS events represents curative outcomes for previously untreated patients with DLBCL: patients avoid experiencing all of disease relapse, disease progression, and death from any cause.

At the 24-Month mark, treatment with Pola+R-CHP resulted in a higher proportion of patients alive and progression-free compared to R-CHOP (76.7% vs 70.2%, respectively). The PFS results from the POLARIX study are considered sufficiently mature and are not likely to change appreciably with longer follow-up based on the magnitude of treatment effect observed with Pola+R-CHP over R-CHOP, and the stability of HR estimates.

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B.2.13 Interpretation of clinical effectiveness and safety evidence

The POLARIX study was a robust Phase III study that included a large global patient population with well-balanced baselines characteristics between treatment arms, standardised chemotherapy regimen, and standardised endpoints powered to show differences between treatment arms. The trial was conducted in accordance with the Good Clinical Practice (GCP) guidelines of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) and the principles of the Declaration of Helsinki (DoH). Safety data was reviewed regularly by an independent Data and Safety Monitoring Committee during the conduct of the trial.

POLARIX met its primary endpoint, demonstrating a statistically significant and clinically meaningful improvement in investigator-assessed PFS following treatment with Pola+RCHP, compared to R-CHOP, in patients with previously untreated DLBCL. Treatment with Pola+R-CHP resulted in a 27% reduction in the risk of progression/relapse, or death, compared with treatment with R-CHOP (stratified HR: 0.73 [95% CI: 0.57–0.95]; two-sided log-rank p-value=0.0177, two-sided =0.05). A clear separation of the Kaplan-Meier curves after 6 months favouring Pola+R-CHP over R-CHOP treatment was observed. The separation was maintained and continued to widen during follow-up. On the basis of KaplanMeier estimates, treatment with Pola+R-CHP resulted in a higher proportion of patients alive and progression-free compared to R-CHOP at 12-Month (83.9% vs 79.8%). The estimated 24-Month INV-assessed PFS was 76.7% in patients treated with Pola+R-CHP compared to 70.2% in patients treated with R-CHOP (absolute difference of 6.5% [95% CI: 0.52–12.5]).

Results of the secondary endpoint EFSeff, were statistically significant and highly consistent with the primary endpoint of INV-assessed PFS results and supportive of clinical benefit for Pola+R-CHP compared with R-CHOP. BICR-assessed CR rates at end of treatment (by PET-CT), although not statistically significant, numerically favour Pola+R-CHP compared to R-CHOP. While there was numeric improvement in the CR-rate by BICR favouring the Pola+R-CHP arm, the durability of responses for patients who achieve a CR, and patients who achieve CR or partial response (PR) favoured the Pola+R-CHP arm compared to R- CHOP in terms of disease-free survival (DFS) and duration of response (DOR) analyses.

The median follow-up in the POLARIX study was 28.2 months, which was not long enough to observe any effect of the PFS benefit on OS. However, other studies have indicated that PFS and 24-Month EFS are often surrogates for OS in patients with DLBCL (63, 64). OS results were immature at this interim analysis and will require longer-term follow-up and patients will be monitored as survival data matures.

The safety profile of Pola+R-CHP in patients with previously untreated DLBCL was generally well-tolerated with manageable toxicities. It was comparable to R-CHOP and in line with the known safety profiles of each individual component and the underlying disease. No new safety signals were identified. The incidence of adverse events of any grade in patients in the Pola+R-CHP arm (97.9%) was comparable with the R-CHOP arm (98.4%). Incidences of any grade AEs, Grade 3–4 AEs, Grade 5 AEs and SAEs were comparable between the treatment arms. Most of the fatal AEs in both arms were due to infections or complications of infection. The incidence of Grade 5 AEs observed in POLARIX was similar to that observed in other randomised Phase III studies involving R-CHOP in 1L DLBCL (e.g. GOYA, PHOENIX and ROBUST ((45), (65), and (66), respectively).

In this double-blind study, drug delivery was not impeded by the replacement of vincristine with polatuzumab vedotin. The delivery of rituximab, doxorubicin, and cyclophosphamide was maintained, with median relative dose intensities of greater than 99% in both treatment groups. Moreover, the percentage of patients who received all the planned doses of polatuzumab vedotin was slightly higher than the percentage who received all the planned doses of vincristine (91.7% in the Pola+R-CHP group and 88.5% in the R-CHOP group), and fewer patients in the Pola+R-CHP group than in the R-CHOP group had adverse events that led to dose reductions.

Regarding the AEPIs discussed in B.2.10.2, the majority of cases of peripheral neuropathy were Grade 1 with similar incidence and severity in the two treatment groups. The occurrence of peripheral neuropathy is expected in patients treated with antibody-drug conjugates containing MMAE and has been described in a study of single-agent polatuzumab vedotin (1) and in studies of polatuzumab vedotin in combination with other agents (67-69).

Although the incidence of febrile neutropenia was higher among patients who received Pola+R-CHP than among those who received R-CHOP in the POLARIX trial (14.3% vs. 8.0%), this finding did not translate into a higher overall incidence of infection, treatment discontinuation, or dose reductions and was similar to the percentages reported in recent R- CHOP trials (9.0–15.2%) (45, 65, 66).

B.3 Cost effectiveness

Summary of cost-effectiveness analysis for Pola+R-CHP vs R-CHOP

• No published economic analyses were identified for polatuzumab vedotin or the comparators in the NICE final scope in DLBCL; therefore, a de novo costeffectiveness model was developed.

• A cure mixture model was built to evaluate the cost-effectiveness of Pola+R-CHP vs. R-CHOP.

• The model possesses a cycle length of 1 week, a lifetime (60 year) time horizon and costs and benefits discounted at 3.5% as per the NICE reference case (70).

• In the base case, R-CHOP was selected as the comparator to Pola+R-CHP, as it was deemed representative of current standard of care for DLBCL patients in the UK, and a robust comparison to Pola+R-CHP using data from the randomised POLARIX trial was possible.

• Survival analysis was performed to identify appropriate parametric survival functions to extrapolate PFS and OS. A cure mixture model was explored, on the basis of clinical expert opinion and evidence from the literature that patients who achieve 2-years PFS following treatment are likely to experience long-term survival aligned with that of the age- and sex-matched general population.

• Based on visual fit to the POLARIX KM data and plausibility of the long-term extrapolations, the generalised gamma distribution was selected for the base case for PFS and OS. The OS extrapolation was informed by the ‘cure fraction’ for PFS.

• Base case utilities were modelled by health state and used from the GOYA trial (weighted). AE disutilities were applied based on CTCAE Grade ≥ 3 AEs from POLARIX and were sourced from recent NICE appraisals. Patients who remained in PFS for >2 years reverted to age- and sex-matched general population utilities.

• Categories of costs included in the model were acquisition, administration, supportive care and subsequent treatment costs. Costs were sourced from NHS Reference Costs 2019-2020, PSSRU 2019, and the BNF and eMIT (both accessed December 2021). Patients who remained in PFS for >2 years no longer accrued supportive care costs. Alternative scenarios were explored in the scenario analysis.

• The base case acquisition costs for polatuzumab vedotin were based on the availability of both 140 mg and 30 mg vials.

• The base case results of the analysis Pola+R-CHP vs R-CHOP produced an ICER of £34,398 per QALY. This was driven by the greater QALY gain vs R-CHOP and

the reduction in supportive care costs and subsequent treatment costs accrued by R-CHOP patients.

• The DSA and scenario analyses demonstrated the robustness of the base case results. The DSA did not identify any parameters that resulted in a significant change in the ICER range relative to the base case. Scenario analyses identified that in general, the ICER value remained relatively unchanged, except where survival modelling extrapolations of reduced clinical plausibility were used. Alternative survival modelling extrapolations statistically, visually and clinically underestimated the long-term survival benefit of Pola+R-CHP and R-CHOP.

• Variation in the PSA from the base case was observed, which may be attributed to the parameter uncertainty around the use of the generalised gamma distribution for modelling survival and the independent variation of input parameters for longterm survival and long-term remission.

• The results of the cost-effectiveness analysis support that Pola+R-CHP is a costeffective treatment option vs R-CHOP, which may be considered representative of standard of care for DLBCL patients in the UK.

B.3.1 Published cost-effectiveness studies

No published cost-effectiveness analyses were available for the technology or comparator regimens identified in the scope. Further details on the methodology and results of the SLR are presented in Appendix H.

B.3.2 Economic analysis

No published cost-effectiveness analyses were available for the technology or comparator regimens identified in the scope, therefore, a de novo economic model was developed using an area under der curve model (AUC) approach to inform decision-making.

B.3.2.1 Patient population

The patient population included in the economic evaluation includes adults with untreated DLBCL. This is in line with the proposed marketing authorisation and the decision problem addressed in this submission. The main body of clinical evidence for Pola+R-CHP compared to R-CHOP is derived from the POLARIX trial (71).

B.3.2.2 Model structure

An AUC or partitioned survival model (PSM) was developed in Microsoft Excel. The AUC model structure is in line with NICE Decision Support Unit (DSU) guidance and is consistent with previous appraisals conducted in the relapse refractory (RR) disease setting. An

important benefit of the partitioned survival approach is that modelling of OS and PFS is based on study-observed events, which is expected to accurately reflect disease progression and the survival profile of patients treated with Pola+R-CHP.

The model includes three mutually exclusive health states: “progression-free (PF)”, “progressed disease (PD)” and “death” as shown in Figure 9.

Figure 9: Economic model structure

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All patients enter the model in the PF health state and remain in this health state until they progress. Upon progression, patients either transition into the PD health state or enter the absorbing health state of death. Patients in the PD health state stay in that health state until death. Patients cannot transition to an improved health state (i.e. from PD to PF), a restriction that is consistent with previous economic modelling in oncology.

The proportion of patients in each health state at any time is defined by the partitioning of alive patients into “PF” and “PD” at discrete time points, based on the PFS and OS curves from POLARIX. The proportion of patients falling into the PF health state is represented by those patients in PFS. The proportion of patients falling into the PD health state is the difference between OS and PFS, as illustrated in Figure 10. The “PD” health state also includes any further lines of treatment, as described in Section B.3.4.

Figure 10: Example of a partitioned survival model

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Key: PSM, Partitioned survival model.

Features of the de novo analysis

For each health state, a specific cost (Section B.3.5) and utility (Section B.3.4.5) is assigned for each time period (represented by a model cycle). Treatment costs are modelled by timeto-off treatment (TTOT) (71). Costs and utilities are multiplied by state occupancy to calculate the weighted costs and quality-adjusted life years (QALYs) per cycle. These can be added across all cycles in the model time horizon to find the total costs and QALYs. These in turn can be used to calculate the model results of the incremental cost per life years gained (LYG) and the incremental cost per QALY gained.

A model cycle length of one week was considered appropriate to reflect the patterns of treatment administration and the transitions to disease progression. Transition between health states can occur at any time within the cycle. In line with previous submissions, a halfcycle correction was applied to mitigate bias. This is also consistent with previous NICE STAs in this disease area.

Costs and health outcomes are discounted at 3.5% and the perspective of the NHS and Personal Social Services (PSS) is assumed, as per the NICE reference case (70). The model inputs for the Pola+R-CHP versus R-CHOP comparison (efficacy, safety and tolerability) are based on the results of the randomised phase III study POLARIX (see Section B.2).

The economic model uses a time horizon of 60 years, which takes into account the age distribution (19–80 years) in the POLARIX trial. Based on the economic model, less than approximately 1% of patients are still alive at 60 years for Pola+R-CHP. Therefore 60 years was deemed appropriate to reflect important differences in costs and outcomes between the technologies being compared. This takes into consideration:

1. Prognosis of patients treated in this setting

2. Expected survival times following present NHS treatment in this setting

3. The maximum plausible impact of improved outcomes following treatment with Pola+R-CHP.

The time horizon is consistent with previous economic models developed for recent NICE submissions in R/R DLBCL as seen in Table 17. Scenario analyses are provided that consider shorter and longer time horizons.

In addition to the time horizon, as DLBCL is a heterogeneous disease that can occur in younger and older patients, the risk of death is modelled using an age distribution cohort approach. Since the POLARIX trial included a broad range of ages (19–80 years), see Figure 11, the age distribution approach was considered more appropriate to reflect the natural disease progression of untreated DLBCL patients. Figure 12 displays the difference between comparing a cohort of only 63-year olds compared with the full age distribution as observed in POLARIX.

Figure 11: Age distribution in the POLARIX trial

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Figure 12: Comparison of the general survival of cohort of 63 years only versus a cohort having the same age distribution as in POLARIX

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Model results are reported in terms of costs per QALY gained, reflecting the decision problem. Table 17 highlights the main features of this economic analysis compared with previous NICE appraisals in R/R DLBCL.

Table 17: Features of the economic analysis

Key: PD, progressed disease; PFS progression free survival; BNF, British National Formulary, DLBCL, diffuse large B cell; SOC, standard of care; OS, overall survival, PSSRU, Personal Social Services Research Unit; eMIT, electronic market information tool.

xB.3.2.3 Intervention technology and comparators

The final scope intervention is Pola+R-CHP, as described in Section B.1.2. For the purposes of this economic evaluation, the primary comparator is R-CHOP.

Intervention: Pola+R-CHP

Pola+R-CHP was included in the model as per the proposed licensed dosing regimen. Pola+R-CHP was modelled to follow the dosing schedule implemented in POLARIX, see Section 3.5, and in accordance with the anticipated marketing authorisation.

Comparator: R-CHOP

In line with the comparator assessed in POLARIX and the current standard of care in the UK, the comparator included in the economic evaluation is R-CHOP. R-CHOP was modelled to follow the dosing schedule implemented in POLARIX, see Section 3.5, and in accordance with the anticipated marketing authorisation.

B.3.3 Clinical parameters and variables

The primary source of clinical data in the economic model for the intervention and comparator is the POLARIX study; a Phase III, randomized controlled trial comparing Pola+R-CHP to R-CHOP. Data from the latest available data cut (June 2021) have been used to inform the clinical parameters for PFS and OS (results data for which are reported in Section B.2.6.3). This study is also the data source for adverse events and treatment for Pola+R-CHP and R-CHOP. All patients had completed treatment with Pola+R-CHP and R- CHOP by June 2021.

B.3.3.1 Survival inputs and assumptions

PFS and OS results from POLARIX were extrapolated to the time-horizon of the model as lifetime results are not available for subjects in the POLARIX study. The minimum PFS follow-up was 24 month and maximum was 37 months. Median OS follow-up was 28 months and maximum was 43 months (28 June 2021 data cut).

Guidance from the NICE DSU was followed to identify base case parametric survival models for OS and PFS (70). Specifically, the following points were performed:

• Visual inspection of the OS and PFS log-cumulative hazard plots, based on patient level data for the two arms of POLARIX, to test for the plausibility of the proportional hazards assumption and to examine the hazard of progression or death in each arm over time.

• The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) goodness-of-fit statistics were calculated to assess statistical fit of the models to both arms of the PFS and OS KM data from POLARIX

• The clinical plausibility of the long-term extrapolations for the base case parametric models was validated by comparing the long-term behaviour of the models with suitable data sources and the expectations of clinical experts.

For both PFS and OS, application of standard parametric survival models (exponential, Weibull, Gompertz, log-normal, Generalised Gamma, Gamma and log-logistic) were explored, in addition to the fitting of cure mixture models. Cure mixture models represent an approach to modelling cancer therapies for which there is evidence to support that a proportion of treated patients enter long-term remission, and subsequently experience mortality aligned with that of the general population. This is reflected in the parameterisation of cure mixture models, which assumes the patient population comprises two subpopulations. The first subpopulation is considered to be cured and at the same risk of mortality as the age- and sex-matched general population (sourced from the Office for National Statistics for this model (77), whilst the mortality rate of the second subpopulation is defined by a selected standard parametric survival curve. The proportion of patients falling into the first population (known as the ‘cure fraction’) is estimated alongside other survival estimates when using a parametric model. The extrapolations for each subpopulation are then combined via the cure fraction to obtain extrapolations for the population as a whole. The cure mixture model adjusts for age, sex and country of the individual patient trial data in order to estimate the hazards linked to background mortality.

Accordingly, evidence to support the exploration of cure mixture survival modelling in the context of this appraisal is as follows:

A study of the natural history of newly diagnosed DLBCL patients treated with immunochemotherapy identified that patients who did not progress or die after two years went on to experience subsequent survival equivalent to that of the age- and sex-matched general population (63). Clinical experts confirmed that patients who achieve two years PFS are at very low risk of subsequent progression, and their risk of death can be assumed to have returned to a level close to that of the matched general population (78). Hence, the cure mixture model is an appropriate method to capture the long-term remission of 1L DLBCL patients who receive R-CHOP or Pola+R-CHP as a treatment. PFS and OS data from the POLARIX study demonstrate that compared to current standard of care, Pola+RCHP is likely to offer patients an improved probability of achieving long-term remission,

which is evidenced by the hazard ratio (HR) of 0.73 (p=0.02) and a clinically meaningful improvement of 6.5% in the number of patients avoiding relapse with Pola+R-CHP vs R- CHOP (71). In addition to clinical expert validation, a cumulative incidence plot is presented in Figure 13, which further confirms the suitability of using the cure mixture model. Finally, precedent of cure mixture modelling in NICE appraisals for R/R DLBCL was established in TA567 (73), TA649 (75) and TA559 (74), where the respective committees accepted that patients who are able to demonstrate sustained remission are likely to benefit from long-term survival.

Cure mixture model

B.3.3.2 Extrapolation of PFS

Proportional hazards assessment: PFS

The validity of the PH assumption between treatments was assessed. This was tested via the visual inspection of the log-cumulative hazard plots. The log-cumulative hazard plot for PFS is presented in Figure 13. It is evident by the non-parallel lines observed for Pola+RCHP and R-CHOP that the ratio of hazard rates between the arms does not remain constant over the follow-up period. Based on this graphical assessment, it can be concluded that the PH assumption does not hold. Therefore, independent parametric models for each treatment arm were fitted for PFS.

Figure 13: Visual check of PH assumption - log-cumulative hazard for INV-PFS IPI 2–5

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Key: PFS, progression-free survival.

Assessment for cure mixture modelling

The suitability of the POLARIX PFS data to the application of cure mixture modelling was assessed. To support the use of cure mixture modelling, the trial data must indicate that a proportion of patients enter long-term remission. Evidence from the literature and clinical opinion suggest that patients with 1L DLBCL remaining progression-free for two years are expected to demonstrate survival aligned with that of the general population. The INV-PFS KM data from the POLARIX trial presented in Figure 14 demonstrates a very low rate of progression for Pola+R-CHP and R-CHOP by the 24-month time point, suggesting a fraction of patients may achieve long-term survival. The assumption will be supported by the upcoming final data cut (June 2022).

Figure 14: KM plot for INV-PFS (POLARIX; data cut: June 2021)

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Statistical fit of models to the observed data

The AIC and BIC goodness of fit results for the functions used to model PFS for Pola+RCHP and R-CHOP in POLARIX, as well as a qualitative impression of visual fit to the observed KM curve are provided below in Table 18.

In the selection of suitable survival functions for PFS, for clinical plausibility, consideration was given to consistency with the extrapolations being explored for OS (discussed in Section B.3.3.3). For all survival functions explored for PFS for both arms, parameterisation for the Weibull and Log-logistic model did not converge. As such, AIC/BIC values are therefore not

presented for these models. Accordingly, the Weibull and Log-logistic extrapolation would ultimately not be selected for OS or as a base case for this economic evaluation.

For the cure mixture models, minimal variation was observed among the statistics. In the Pola+R-CHP arm, functions typically either overestimated PFS in the earlier months and/or underestimated the decline in patient progression towards the end of follow-up. Of all the functions, the Generalised Gamma, Gamma and log-normal provided the most reasonable fit in the Pola+R-CHP arm. In the R-CHOP arm, the Generalised Gamma, log-logistic and lognormal appeared to fit the observed data reasonably well. The cure mixture model appropriately captures the survival benefit of Pola+R-CHP and R-CHOP beyond the 24Month time point as seen in Figure 15 and Figure 16, which is consistent with clinical expert opinion and external sources (79). Table 18 presents the cure fractions (i.e. the proportion of patients achieving long-term remission) predicted by each of the cure mixture extrapolations for both arms. With exception of Gompertz distribution, the proportion of patients achieving long-term remission falls into a narrow range from 71–76% in the Pola+R-CHP arm, and 60– 70% in the R-CHOP arm. A narrow range of values demonstrates consistency in the cure fraction estimation across parametric models, further supporting the suitability of this modelling approach.

Based on visual fit, plausibility of the long-term extrapolation, and alignment with the selected OS distribution (see Section B.3.3.3), the cure mixture Generalised Gamma survival curve was selected for the base case for both arms, whilst the log-normal and exponential extrapolations were explored in scenarios. The final base case extrapolations are shown alongside the selected OS extrapolation in Figure 24 in Section B.3.3.4, where the long-term plausibility of the selected extrapolations for both outcomes is also discussed.

Table 18: Ranking of PFS distribution for Pola+R-CHP and R-CHOP based on AIC, BIC, long-term survival estimate and assessment of

their visual fit.

Key: AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; ✔ symbol indicates a model has a good fit to the KM data; A ~ symbol indicates a model has an average fit to the KM data; a × indicates a model has an unsuitable fit to the KM data.

Figure 15: PFS cure mixture extrapolation functions - Pola+R-CHP

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Figure 16: PFS cure mixture extrapolation functions - R-CHOP

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Figure 17: PFS mixture-cure extrapolation functions – +Pola+R-CHP and R-CHOP

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B.3.3.3. Extrapolation of OS

Proportional hazards assessment: OS

In line with PFS, the proportional hazards assumption does not hold for OS. This is evident by the diverging lines between Pola+R-CHP and R-CHOP that can be observed in Figure 18, indicating that the ratio of hazard rates between arms does not remain constant over the follow-up period. Therefore, independent parametric models for each treatment arm were fitted for OS.

Figure 18: Visual check of PH assumption - KM OS

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OS extrapolation

Due to the immaturity of the OS data and limited OS events, it was not possible to directly estimate the proportion of patients who would experience long-term survival. As a result, with guidance provided by clinical experts and assuming that patients who have not yet progressed can be considered to be long-term survivors, OS was informed by the long-term remission fraction (i.e. the OS cure fraction was informed by the PFS cure fraction).

Figure 19: KM plot for INV OS (POLARIX; data cut: June 2021)

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The Generalised Gamma, exponential, log-normal,Gompertz and Gamma distributions were fitted to both arms as seen in Table 19. Since the log-logistic and Weibull distributions did not converge for the PFS extrapolations, they were not presented in the OS extrapolation.

As was the case for PFS when applying a cure mixture model, AIC and BIC values indicated a similar statistical fit to the KM and IPD data for both arms. Based on the AIC and BIC values and the visual fit for Pola+R-CHP, the best fitting parametric model for OS is the lognormal, Generalised Gamma and Gompertz curve. Based on the AIC and BIC values and visual fit for R-CHOP, the best fitting parametric model for OS is the Generalised Gamma curve.

All parametric models were also assessed for visual fit to the Kaplan-Meier data as seen in Figure 20 and Figure 21. Most of the parametric distributions underestimate the long-term benefit of Pola+R-CHP and R-CHOP. However, in Figure 20 we can observe that the Gompertz, Generalised Gamma and Log-normal provide the best visual fit since a plateau can be observed towards the end of the curve, which is expected to be seen with Pola+RCHP and R-CHOP. Figure 22 shows the combined OS extrapolations for Pola+R-CHP and R-CHOP.

Table 19: Ranking of OS distribution for Pola+R-CHP and R-CHOP based on AIC, BIC, long-term survival estimate and assessment of their visual fit

Key: AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion. ✔ symbol indicates a model has a good fit to the KM data; A ~ symbol indicates a model has an average fit to the KM data; a × indicates a model has an unsuitable fit to the KM data.

Figure 20: Visual fit of OS distributions to POLARIX data - Pola+R-CHP (informed by

PFS)

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Figure 21: Visual fit of OS distributions to POLARIX data - R-CHOP (informed by PFS)

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Figure 22: OS standard extrapolation functions – Pola+R-CHP and R-CHOP (informed by PFS)

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Based on the fit to the observed data of all extrapolations in both the Pola+R-CHP and R- CHOP arms, the Generalised Gamma offered the best fit based on statistical ranking

(AIC/BIC) and visual fit, reaffirming Generalised Gamma as the most appropriate extrapolation for the base case. The log-normal and exponential distributions were investigated as scenario analyses. Once AIC and BIC was evaluated and the visual fit for the Pola+R-CHP and R-CHOP curves were assessed, the cure rate for each extrapolation was evaluated. The cure rates predicted by each model are presented in Table 19. As previously described, it was assumed that patients who have not progressed with or died from DLBCL can be considered long-term survivors and can be expected to follow the age and gender matched survival in general population. The PFS data produced an estimated proportion of patients with long-term remission ranging from 0% to 76% and 0% to 70% in the Pola+RCHP arm and the R-CHOP arm, respectively and these estimates were used to inform the long term survival when fitting the mixture cure rate model for OS. The OS improvement in the Pola+R-CHP arm can be attributed to the increase in patients who are considered in remission after 2 years and are in long-term remission, as validated by clinicians and

external data sources such as the GOYA trial (80). As a result, based on the cure mixture model using the Generalised Gamma distribution, we can estimate that 75% of patients who receive Pola+R-CHP will experience long-term survival compared to 64% who receive R+CHOP as shown in Table 19.

External validation with the GOYA trial data (adjusted)

Whilst statistical tests and visual inspection are useful in determining which models best fit the observed data, they cannot provide information on how suitable a parametric model is for the time-period beyond the final trial follow-up. Therefore, the clinical validity of the selected extrapolations for PFS and OS was assessed by comparing the long-term predictions of the model with long-term outcomes expected in clinical practice.

As mentioned in Section 3.3.2, the GOYA trial (80), which was adjusted for imbalances in patient characteristics and imbalances, was used to assess the clinical validity of the selected extrapolations for PFS and OS.

A 5-Year R-CHOP PFS rate is available from the GOYA clinical trial final data cut. As seen in Figure 23, the Generalised Gamma long-term survival estimate for adults with untreated DLBCL in the POLARIX R-CHOP arm is aligned with the long-term survival estimate (KM data) from the GOYA R-CHOP arm (adjusted) (64% vs 64%). Therefore, the Generalised Gamma distribution can be considered the most appropriate distribution for both treatment arms to model PFS and OS in the POLARIX trial. As the R-CHOP PFS rate in POLARIX and GOYA align, we can deduce that the estimate of 75% patients who receive Pola+R-CHP will experience long-term remission is accurately estimated. Figure 23 shows the visual fit of the GOYA R-CHOP KM curve, POLARIX R-CHOP extrapolation (Generalised Gamma) and POLARIX Pola+R-CHP extrapolation (Generalised Gamma).

Figure 23: PFS extrapolation with the cure mixture model (Generalised Gamma distribution)

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B.3.3.4. Base case extrapolations of PFS and OS

Following NICE advice (81), it was deemed most appropriate to select the same distribution to model PFS and OS in both treatment arms. Based on all aspects of the curve selection mentioned above, the Generalised Gamma curves were selected as the most clinically plausible curves to represent both Pola+R-CHP and R-CHOP for PFS and OS and were therefore used in the economic model base case. Alternative curve choices were investigated in scenario analyses.

Figure 24 demonstrates the base case for PFS and OS for the POLARIX ITT population using the cure mixture model.

Figure 24: Base case for PFS and OS for the ITT population (cure mixture model)

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Key: PFS, progression-free survival, OS, overall survival. Gen gam, Generalised Gamma, ITT, Intention to treat.

B.3.3.5 Treatment duration in the economic model

All patients in the POLARIX study completed their planned treatment by the time of data cut off (June 2021). The rate of treatment discontinuation was low in both treatment arms as seen in Figure 25 and Figure 26. The most common reason for treatment discontinuation in the R-CHOP arm was due to adverse events n=17 (3.9%) and the progression of disease n=17 (3.9%). In the Pola+R-CHP arm, the corresponding numbers were n=9 (2.0%) and n=12 (2.7%). Dose reduction was more common in the R-CHOP arm n=51 (11.6%) vs n=30 (6.9%) in the Pola+R-CHP arm. Any uncertainty around the treatment duration was captured in the probabilistic sensitivity analysis (PSA) using the Greenwood formula.

Figure 25: Kaplan-Meier curves showing time-to-off treatment (TTOT) for Pola+R-CHP

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Figure 26: Kaplan-Meier curves showing TTOT for R-CHOP

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Table 20 and Table 21 show the time-to-off treatment duration and average treatment cycle

for patients in the POLARIX trial.

Table 20: Time-to-off treatment in months for Pola+R-CHP and R-CHOP (POLARIX)

Table 21: Average treatment cycles for Pola+R-CHP and R-CHOP (POLARIX)

B.3.3.6 Adverse events

All Grade ≥3 adverse events for Pola+R-CHP and R-CHOP, with an incidence of ≥2% in at least one treatment arm were sourced from the POLARIX clinical study (data cut-off, June 2021). Duration of AE data were sourced from TA306. Disutilities and costs were applied for each AE in the relevant arm (see Sections B.3.4.4 and B.3.5.3., respectively) (72). Adverse events included in the model are outlined below in Table 22.

Table 22: POLARIX adverse events included in the economic model (events occurring

at Grade 3–5, affecting 2% or more of patients)

B.3.4 Measurement and valuation of health effects

Health-related quality-of-life (HRQoL) data for the model health states were based on values sourced from the GOYA trial (see Section B.3.4.5) (80).

B.3.4.1 Health-related quality-of-life data from clinical trials

HRQoL data were collected in the POLARIX study directly from first line DLBCL patients via the EQ-5D-5L questionnaire. EQ-5D-5L results were mapped to EQ-5D-3L, using the van Hout algorithm (82). The base case model uses the weighted GOYA trial utilities as referenced in Section 3.4.5.

B.3.4.2 Mapping

The POLARIX phase III randomised controlled trial collected EQ-5D-5L, which were mapped to EQ-5D-3L, using the van Hout algorithm.

B.3.4.3 Health-related quality-of-life studies

A SLR was conducted to identify HRQoL evidence in the treatment of patients with 1L DLBCL.

B.3.4.4 Adverse reactions

All grade ≥3 adverse events for Pola+R-CHP and R-CHOP, with an incidence of ≥2% in at least one treatment arm were sourced from the POLARIX study.

There are two approaches that could be taken regarding the inclusion of adverse event impacts on HRQoL:

1. The assumption that any disutility has already been incorporated into the base case health state utilities through trial derived EQ-5D utilities, and incorporating an additional disutility could be considered double counting.

2. The assumption that averaged trial-derived utilities underestimate disutilities associated with adverse events, and therefore an additional disutility must be applied.

Despite the POLARIX trial collecting adverse events, the base case analysis takes the latter assumption since the GOYA trial utility values were used in the base case.

Disutilities were sourced from published literature and the NICE appraisal TA306 (72). As discussed in Section B.3.3.5, treatment-related AEs of CTCAE Grade 3 or higher that were deemed to be serious were included in the model. Disutilities from AEs for the respective treatments were applied by multiplying the disutility with the duration of AEs. This approach aligns with the rationale that AEs are treatment related and occur soon after treatment initiation. A summary of the adverse events and QALY losses included in the economic model is provided in Table 23.

Table 23: Summary of adverse events and disutilities included in the economic model (events occurring at Grade 3–5, affecting 2% or more of patients)

Key: AE, adverse evets; SE, standard error.

B.3.4.5 Health-related quality-of-life data used in the cost-effectiveness

analysis

As mentioned in Section B 3.4.1, HRQoL data were collected in the POLARIX study via the EQ-5D-5L questionnaire and mapped to the EQ-5D-3L using the van Hout algorithm (82). Once the POLARIX utility values were mapped, the POLARIX and the weighted GOYA trial utility values were validated with clinicians. The weighted GOYA trial utility values were considered more appropriate than the mapped POLARIX utility values for two reasons: longer follow-up data was available for the GOYA trial and the GOYA trial utility values where more reflective of the quality of life observed in 1L DLBCL patients.

As a result, the GOYA trial utility values were deemed a reliable and appropriate source for informing this cost-effectiveness analysis. The mapped POLARIX utility values are explored as a scenario analysis.

In agreement with the assumptions adopted in TA559 (74), TA649 (75) and TA567 (73), in the base case, patients who remain in the PFS state for two years revert to age- and sexmatched general population utilities for the UK, which are based on Ara and Brazier 2010 (83). This is further evident in Launonen A et al 2021 (84), who demonstrated that after 1L DLBCL patients reach PFS24, their quality of life returns to the same level as the general population, adjusted for country and age. This assumption aligns with clinical expert feedback on the natural history of DLBCL and evidence from Jakobsen LH et al. 2017 (63), who suggested that patients who achieve sustained remission for up to two years are considered to experience mortality rates and quality of life aligned to that of the general population. It is therefore assumed that a similar utility to the general population is accrued in these patients. A scenario analysis has been conducted returning the quality of life to the same as the general population after 3 years.

A summary of utility values used in the economic model is provided in Table 24. Table 25 shows the utility values that will be applied in scenario analyses to test the robustness of this economic evaluation.

Table 24: Summary of utility values for cost-effectiveness analysis

In agreement with the assumptions adopted in TA559 and TA567, in the base case, patients who have remained in the PFS state for two years revert to age- and sex-matched general population utilities for the UK, which were based on Ara and Brazier 2010 (83). In Age- and sexaddition, Launonen A et al 2021 (84) matched general demonstrated that after patients reach population utility PFS: longPFS24, their QoL returns to the same level values from Ara N/A term follow up and Brazier 2010 as the general population, adjusted for (112) country and age. This assumption aligns with clinical expert feedback on the natural history of DLBCL and evidence from Jakobsen LH et al. 2017 (as discussed in Section B.3.3.1), that patients who achieve sustained remission for up to two years are considered to experience long-term survival aligned to that of the general population. Treatment Disutility values sourced from relevant NICE appraisal TA306 and the literature. disutilities

Key: PD, progressed disease; PF, progression-free.

Table 25: Summary of utility values for cost-effectiveness analysis (scenario analyses)

B.3.5 Cost and healthcare resource use identification, measurement and valuation

An SLR was conducted to identify studies presenting novel cost and resource use data associated with 1L DLBCL, relevant to the economic model presented herein. Detailed descriptions of the search strategy, search terms and extraction methods, as well as details of the included studies, are provided in Appendix J. In total, 18 publications met the SLR inclusion criteria.

Parameter input costs for the model were derived from NHS reference costs (85), electronic Marketing Information Tool (eMIT) (86) and the British National Formulary (BNF) (87).

B.3.5.1 Intervention and comparators’ costs and resource use

The economic analysis was conducted from the NHS and PSS perspective, with appropriate unit cost sources, such as NHS reference costs (2019–20) (85), eMIT (86), and BNF online (87) used to inform model cost inputs.

The following resource use and cost elements were included for the PFS and PD health states present in the model:

• PFS: drug acquisition and administration, treatment-related AEs and routine supportive care (professional and social services, health care professionals and hospital resource use, treatment follow-up) and subsequent treatment costs

• PD: drug acquisition and administration (for further interventions received), supportive care (professional and social services, health care professionals and hospital resource use, treatment follow-up) and subsequent treatment costs

The assumptions used for deriving the resource use and costs for supportive care in both PFS and PD health states were aligned with those specified in previous NICE submissions TA306 (72), TA649 (75) and TA559 (74). Based on the ESMO guidelines recommending routine follow-up of up to 24 months (79), it is assumed that patients remaining PFS for two years are discharged and therefore do not incur further supportive costs associated with DLBCL. Clinicians validated the assumption that patients are considered to be in remission after 2 years in the PFS state. Additional scenario analyses varying the time point at which patients are discharged have been included in Section 3.8.3.

B.3.5.1.1 Drug acquisition costs

Drug acquisition costs for the treatments included in the economic model are summarised in Table 26. For medicines available to the NHS as generic medicines, prices are taken from eMIT, which reports the average price paid by the NHS for a generic medicine for the last period (85). For medicines only available to the NHS as proprietary medicines, prices are taken at the list price stated in the BNF (87). For pola, a patient access scheme (PAS) is currently available which offers a simple discount of xxxx

Pola+R-CHP drug acquisition costs and dose calculations

Drug acquisition costs and the cost per cycle for Pola+R-CHP are presented in Table 26. For the Pola+R-CHP regimen, patients are assumed to receive up to six cycles (21 days per cycle) of Pola+R-CHP, administered at mean doses of 1.8 mg/kg on Day 1 of each cycle for polatuzumab vedotin, 375 mg/m2 on Day 1 of each cycle for rituximab (both on Day 1 of each cycle), 750 mg/m[2] on Day 1 of each cycle for cyclophosphamide,50 mg/m[2] on Day 1 of each cycle for doxorubicin, 100 mg/day PO for prednisolone (on Days 1-5 of every 21-day) and 2 cycles of rituximab monotherapy, 375 mg/m[2] for rituximab (both on Day 1 of each cycle). The mean treatment doses were derived from the weight (75.92kg) and body surface area (BSA) (1.86 m[2] ) distribution of patients enrolled in the POLARIX study.

Costs per cycle in the model base case are calculated based on the availability of 140 mg and 30 mg vials under the conservative assumption of ‘no vial sharing’. Based on the weight distribution of patients enrolled in the POLARIX study, a dose of 1.8 mg/kg resulted in a mean per cycle cost of £11,604.89 per cycle at list price and xxxxxxxxx with a PAS of xxxx

Rituximab is available as a biosimilar at a list price of £157.17 for the 100 mg vial and £785.84 for the 500 mg vial (Rixathon®/Truxima®, BNF 2021) (88). In the base case, an estimated discount of 50% was applied to the biosimilar rituximab list price, based on the national tendering process for rituximab biosimilar medicines (precise discount values are kept in confidence by the NHS). The rituximab dose is calculated based on the BSA distribution of the POLARIX patient cohort. Patients were assumed to receive a dose of 375 mg/m2 of rituximab administered on Day 1 of each cycle. Assuming no vial sharing, the average cost per cycle for rituximab was calculated to be £582.09.

Cyclophosphamide is now available as a generic formulation of 500 mg and 2000 mg at a cost of £8.21 and £28.22 per vial, respectively (BNF) (89). Patients were assumed to receive 750 mg/m[2] for cyclophosphamide (on Day 1 of each cycle) based on the BSA distribution of the POLARIX cohort. Assuming no vial sharing, the cost per cycle for cyclophosphamide was calculated to be £28.26.

Doxorubicin is now available as a generic formulation of 10 mg and 200 mg at a cost of £2.83 and £20.02 per vial, respectively (86). Patients were assumed to receive 50 mg/m[2] on Day 1 of each cycle based on the BSA distribution of the POLARIX cohort. Assuming no vial sharing, the cost per cycle for doxorubicin was calculated to be £20.02.

Prednisolone is now available as a generic formulation of 5 mg and 25 mg at a cost of £0.41 per dose and £17.72, respectively (86). Patients were assumed to receive 100 mg/day PO

for prednisolone (on Days 1-5 of every 21-dayThe cost per cycle for prednisone/prednisolone was calculated to be £1.64.

Comparator drug acquisition costs and dose calculations

Drug acquisition costs for R-CHOP are presented in Table 26, with calculations for the per cycle cost of cyclophosphamide, doxorubicin, and prednisolone remaining the same as those specified for Pola+R-CHP.

Vincristine is now available as a generic formulation of 1 mg at a cost of £3.43 and 2 mg at a cost of £6.48 per dose, respectively (86). Patients were assumed to receive 1.4 mg/m[2] IV on Day 1 of each cycle. Assuming no vial sharing, the cost per cycle for vincristine was calculated to be £10.18.

Table 26: Treatment acquisition costs (with PAS)

Key: BNF, British National Formulary; eMIT, electronic market information tool.

B.3.5.1.2 Drug administration costs

Administration costs for chemotherapy included in the model are presented in Table 27, with the unit cost per resource as reported in the NHS reference cost schedule 2019–20 (85).

Pharmacy costs for the preparation of IV infusions were not considered separately in previous TAs in R/R DLBCL (72, 73, 75), likely on the basis that there is no unbundled NHS tariff to cover pharmacy service costs in relation to the preparation of IV infusions. In this analysis, it was assumed that the preparation of each cycle of a regimen containing polatuzumab vedotin or rituximab required 39 minutes of pharmacy time, as estimated in a UK-based time and motion study of rituximab in non-Hodgkin’s lymphoma (118). An hourly cost for a hospital pharmacist is £48 (90), resulting in a per cycle cost of £31.20. This is consistent with the approach taken in TA649 (75).

Table 27: NHS reference costs 2019–20 for chemotherapy administration

Key: HRG, Health Resource Group.

The total per cycle drug administration costs for the Pola+R-CHP and R-CHOP treatment regimens are summarised in Table 28. For Pola+R-CHP and R-CHOP, the administration costs are separated into administration costs for the first administration (first cycle) and subsequent administrations (subsequent cycles). Expected costs per treatment cycle were calculated using weekly average treatment costs and TTOT KM data (Section B.3.3.3).

Table 28: Drug administration costs per cycle

B.3.5.2 Health-state unit costs and resource use

Supportive care costs

The type and frequency of resource utilisation in the PFS and PD health states is based upon data from the manufacturer’s submission for TA306 (72), which were derived from questionnaire responses from a set of UK physicians selected based upon publication record in the field of aggressive non-Hodgkin’s lymphoma (NHL), prior collaboration, and referrals from other physicians. The resources listed consist of three separate categories (professional and social services, healthcare professionals and hospital resource use, and treatment follow-up). Table 29 presents the cost per unit for each type of resource included in the model, whilst Table 30 presents the annual frequency of resource use in each health state. Where required, resource use frequency per model cycle was calculated from the 28day frequency values as below:

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Table 29: Supportive care resource use unit costs included in the model

Health care professionals and hospital resource use

Key: PF, progression-free; CT, computed tomography; ECG, electrocardiogram; ERG, Evidence Review Group; GP, General Practitioner; LDH, lactate dehydrogenase test; MRI, magnetic resonance imaging; MUGA, multiplegated acquisition scan; PD, progressed disease; PET-CT, positron emission tomography–computed tomography; PFS, progression-free survival, PD, progressed disease.

Resource use was assumed to be the same for both arms, in accordance with clinical expert opinion (78). For the PFS health state, resource use was specified for patients whilst they were on- or off-treatment. Clinical expert opinion also considered that patients remaining in PFS for longer than two years were in long-term remission, and it was therefore assumed that no additional supportive costs were incurred beyond this time point (79). A scenario analysis was conducted that included additional supportive care costs beyond the 2-year time point.

Based on the unit costs and the annual frequencies presented above, the average per cycle supportive care costs for each health state were calculated as shown in Table 31.

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Table 30: Annual frequency of resource use in PFS (on and off treatment) and PD

a TA306 (72). b One-off costs weighted by the proportion of patients requiring the respective resource. Key: CT, computed tomography; ECG, electrocardiogram; ERG, Evidence Review Group; GP, General Practitioner; LDH, lactate dehydrogenase test; MRI, magnetic resonance imaging; MUGA, multiple-gated acquisition scan; PD, progressed disease; PET-CT, positron emission tomography–computed tomography; PFS, progression-free survival; PD, progressed disease.

Table 31: Per cycle supportive care costs for PFS and PD health states

B.3.5.3 Adverse reaction unit costs and resource use

All Grade ≥3 treatment-related adverse events with an incidence of ≥2% in either the Pola+R-CHP or R-CHOP arms of the POLARIX study are included in the base case analysis.

The costs of treating adverse events are accounted for per episode. Where possible, the NHS Reference Costs (2019/2020) were applied to adverse events.

The cost of adverse events for each treatment arm is calculated by multiplying total frequency of adverse events by the unit cost. Adverse event costs are applied as a one-off cost in the first cycle of treatment only, hence it is assumed that the adverse event occurs at treatment initiation, only once across the time horizon of the model.

The frequency and unit costs associated with the management of the identified AEs are presented in Table 32.

Table 32: Resources associated with adverse events

Key: NHS, National Health Service.

B.3.5.4 Subsequent treatment costs

Data on the type and duration of subsequent treatment was available in POLARIX. Time to new anti-lymphoma (NALT) therapy was used to estimate the patients who would undergo subsequent lines of therapy after first-line treatment with Pola+R-CHP and R-CHOP. The model only considered subsequent treatments that could be classified into the following categories, which reflect what is currently used in UK clinical practice.

• Chemotherapy

• Chemotherapy + R

• ASCT

• Salvage Therapy (intention to proceed with transplant)

• Salvage Therapy + R (intention to proceed with transplant)

• Rituximab monotherapy

• Pola+R-CHP

• Bridging treatment + CAR-T

• Allogeneic hematopoietic cell transplant

The costs of subsequent lines of therapy are included in the progressed disease health state of the model. Although data on the treatment and duration of subsequent therapy were collected in POLARIX, these were not considered fully representative of UK clinical practice. As a result, clinical input was collected to determine a more representative breakdown of subsequent treatments. Values obtained from clinicians are used in the base case, and values from the POLARIX trial are explored in a scenario analysis.

The proportion of each subsequent systemic treatment received are represented in Table 33. The average number of systemic treatments received after 1L are represented in Table 34.

Table 33: Subsequent systemic treatment (UK clinical input)

Table 34: Number of subsequent systemic treatments after 1L

The cost of autologous and allogeneic SCT included two components: stem cell harvesting and the SCT procedure. The cost of stem cell harvesting and the cost of the procedure were based on NHS reference costs as seen in Table 35 (85). The cost of stem cell harvesting was estimated based on the weighted average of elective SA18Z Bone Marrow Harvest and SA34Z Peripheral Blood Stem Cell Harvest.

Table 35: SCT cost inputs

Key: HRG, Health Resource Group. ASCT, autologous stem cell transplant, AlloSCT, allogeneic stem cell transplant.

The subsequent treatment administration cost per cycle are summarised in Table 36.

Table 36: Subsequent treatment administration costs

Total cost of subsequent treatments

The total cost of subsequent treatments was applied as a one-off cost at the time point of

progression in the model. Table 37 shows the pooled cost for each treatment arm based on the POLARIX trial.

Table 37: Subsequent treatment costs based on POLARIX data

B.3.5.5 Miscellaneous unit costs and resource use

A separate cost of death was not applied in the model as it was assumed the costs for supportive care after progression would be accounted for in the cancer-related palliative care costs for progressed patients. Cost and resource use for death from other causes is not included in the model.

B.3.6 Summary of base-case analysis inputs and assumptions

B.3.6.1 Summary of base case analysis inputs

A table summarising the full list of variables applied in the economic model is presented in Table 38.

Table 38: Summary of variables applied in the economic model base case

Key: AE, adverse event; BSA, body surface area; CI, confidence internal; KM, Kaplan Meier; OS, overall survival; PD, progressed disease; PFS, progression-free survival.

B.3.6.2 Assumptions

A table summarising the key assumptions in the economic model is presented in Table 39.

Table 39: Key assumptions used in the economic model

Key: AE, adverse events; BS, biosimilar DLBCL, diffuse large B-cell lymphoma; ESMO, European Society for Medical Oncology; ITT, intent-to-treat; IV, intravenous; KM, Kaplan-Meier; NHS, National Health Service; NICE, National Institute for Health and Care Excellence; OS, overall survival; PFS, progression-free survival; RCT, randomised controlled trial, TA, technology appraisal; UK, United Kingdom, AIC, Akaike information criterion; BIC, Bayesian information criterion; EQ-5D, EuroQol- 5 Dimension; HRQoL, health-related quality of life, TTOT, time-to-off treatment .

B.3.7 Base-case results

B.3.7.1 Base case incremental cost-effectiveness analysis results

The base case pairwise comparison results for Pola+R-CHP vs R-CHOP are presented in Table 40. The clinical outcomes and disaggregated base case cost-effectiveness results are presented in Appendix K.

The base case cost-effectiveness results demonstrate that Pola+R-CHP is cost-effective vs R-CHOP, at an incremental cost-effectiveness ratio (ICER) of £34,398 per QALY with a PAS of xxxx Pola+R-CHP accrued a greater health benefit compared to R-CHOP, as demonstrated by a QALY gain of xxxxxx

Table 40: Base case results (with PAS for POLIVY)

Key: ICER, incremental cost-effectiveness ratio; LYG, life years gained; PAS, patient access scheme; QALYs, quality-adjusted life years.

B.3.8 Sensitivity analyses

B.3.8.1 Probabilistic sensitivity analysis

The uncertainty arising from the imprecision associated with model input parameter estimates was investigated via probabilistic sensitivity analysis (PSA). A Monte-Carlo simulation was conducted using 2,000 iterations based upon model inputs randomly drawn from distributions around the mean (summarised in Table 41). Variation in the parameterisation of the PFS and OS extrapolations was based on normal distributions and where appropriate, covariance matrices.

Where available, the standard error (SE) calculated from the same data used to derive the mean value estimate was used to inform the distribution of the input parameter. Alternatively, the SE was calculated for AE disutility inputs as 10% of the mean estimate, or for cost inputs via the following equation:

SE = (LN(mean + 20%) −LN(mean −20%))/4

Table 41: PSA parameter inputs

Key: CT, computed tomography; ECG, electrocardiogram; GP, General Practitioner; LDH, lactate dehydrogenase test; MRI, magnetic resonance imaging; MUGA, multiple gated acquisition scan; N/A, not applicable; OS, overall survival; PD, progressed disease; PET-CT, positron emission tomography-computed tomography; PFS, progression-free survival; PSA, probabilistic sensitivity analysis; SE, standard error.

The results of the PSA are presented in Table 42. The mean incremental costs and QALYs

from the PSA were xxxxxxx and xxxxx respectively, resulting in a mean ICER value of £33,727 per QALY.

Table 42: Mean probabilistic results (with PAS)

Key: LYG, life years gained; QALY, quality of life; ICER incremental cost-effectiveness ratio.

The cost-effectiveness plane is presented in Figure 27. The cost-effectiveness acceptability curve (CEAC) for Pola+R-CHP versus R-CHOP is presented in Figure 28. From the CEAC,

at a willingness to pay (WTP) threshold of £50,000, the probability of Pola+R-CHP being cost-effective relative to R-CHOP was xxxxxx

Figure 27: Cost-effectiveness plane for Pola+R-CHP vs. R-CHOP

x xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxx x xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxzxxxxx xxxxxx xxxxxx xxxxxx xxmmxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxz

Figure 28: Cost-effectiveness acceptability curve for Pola+R-CHP vs R-CHOP

x xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxx x xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxzxxxxx xxxxxx xxxxxx xxxxxx xxmmxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxxx xxxxz

B.3.8.2 Deterministic sensitivity analysis

A deterministic sensitivity analysis (DSA) was performed to investigate key drivers of the base case results. Each input parameter was set to its respective upper or lower bound and the deterministic results for the model recorded. Generally, the base case parameter values were varied across their 95% CI. The parameter values used in the DSA are displayed in Table 43.

Table 43: Parameter values used for DSA (with PAS for POLIVY)

Key: DSA, deterministic sensitivity analysis; ICER, incremental cost-effectiveness ratio; PAS, patient access scheme; PD, progressed disease; PFS, progression free survival; AE, adverse events.

A tornado diagram demonstrating the key drivers of the ICER are presented in Figure 29. As shown below, the three parameters most influential on the model ICER value were supportive care costs in PD for R-CHOP, supportive care costs in PD for Pola+R-CHP and the average patient age at baseline.

Figure 29: Tornado diagram, Pola+R-CHP vs R-CHOP

==> picture [512 x 167] intentionally omitted <==

B.3.8.3 Scenario analysis

Scenario analyses were conducted to assess uncertainty around the assumptions in the model. The list of scenarios explored in the model are listed in Table 44. Results including the Polivy PAS are presented in Table 45.

Deterministic ICER values from the scenario analyses listed ranged by -31.09% to 75.54%. The three most influential (groups of) scenarios were the application of standard parametric survival modelling, the application of the log-normal distribution to the cure mixture model and the application of the POLARIX IPI 2–5 utilities, which resulted in approximate increases to the ICER of 75.54%, 33.83% and 14.56% relative to the base case. These scenarios are discussed below. No other scenario exceeded a change in the ICER of over 75.54%.

Table 44: Parameters varied in the scenario analysis

Key: OS, overall survival; PD, progressed disease; PF, progression-free; PFS, progression-free survival.

Table 45: Parameters varied in the scenario analysis

Key: OS, overall survival; PD, progressed disease; PF, progression-free; PFS, progression-free survival, BSA, Body Surface Areal.

Standard parametric model

The application of standard parametric models for PFS and OS extrapolation produced a change in ICER value of 75.54%. It should be noted, however, that the standard parametric extrapolations are less clinically plausible with regards to long-term patient survival in DLBCL. As seen in Figure 23, the long-term survival estimated by the cure mixture model aligned with the long-term survival KM data observed in clinical practice. In addition, the standard parametric survival models do not directly capture patients who go on to achieve sustained remission and subsequent long-term survival following treatment. Therefore, relative to the base case, application of the standard parametric extrapolations is likely to underestimate the survival benefit and thus the cost effectiveness of Pola+R-CHP vs R- CHOP.

Excess mortality

An increase in the excess mortality value of long-term remission DLBCL patients from 1 to 1.1 would be expected to result in a difference in ICER value of 8.85% relative to the base case. This is on the basis that long-term remission DLBCL patients after 2 years will not have the same mortality rate as the general population, following treatment with Pola+RCHP (as discussed previously). Increasing the excess mortality from long-term remission DLBCL patients from 1 to 1.1 is not reflective of what is seen in the literature, therefore, achieving the same mortality rates as the general population after two years is used as the base case.

Time horizon

A reduction in time horizon would be expected to result in a difference in ICER value relative to the base case. This is on the basis that a proportion of patients are likely to achieve sustained remission and long-term survival following treatment with Pola+R-CHP. A short time horizon would therefore not capture the full benefits derived from treatment with Pola+R-CHP; therefore, a lifetime horizon of 60 years was used as the base case.

Supportive care costs

An increase in the incurred supportive care costs from 2 years to 3 years would be expected to result in a difference in ICER value of 2.71% relative to the base case. This is on the basis that a proportion of patients will incur supportive care costs for longer after achieving sustained remission and long-term survival following treatment with Pola+R-CHP. Increasing the supportive care costs from 2 years to 3 years is not reflective of UK clinical practice, so the base case applies the two-year time point.

Subsequent treatment

The application of the POLARIX subsequent treatment breakdown produced a change in ICER value of -4.31%, which resulted in a minimal change relative to the base case. However, implementing UK clinical input subsequent treatment breakdown is more reflective of UK clinical practice and was therefore used in the base case.

B.3.8.4 Summary of sensitivity analyses results

From the PSA, at a willingness to pay (WTP) threshold of £50,000, the probability of Pola+RCHP being cost-effective relative to R-CHOP was xxxxxx Variation in the PSA may be attributed to the parameter uncertainty around the use of the Generalised Gamma distribution for modelling survival and the independent variation of input parameters for longterm survival and long-term remission.

Whilst the DSA identified that the supportive care costs in the PD health state for Pola+RCHP and R-CHOP as well as the average patient age at baseline had the greatest impact on the ICER, none was found to result in a range of ICER values greater than a total of £8724.42.

The scenarios considered in Section B.3.8.3 resulted in ICER values that ranged £9,439 to £60,385. It was noted that standard parametric survival modelling resulted in the largest deviation from the base case ICER value. However, as discussed previously, these models were considered to lack clinical plausibility and did not represent the observed trial data relative to the cure-mixture approach with respect to their ability to reflect long-term patient remission and survival. The application of the standard parametric survival modelling, the log-normal distribution to the cure mixture model and the use of the POLARIX IPI 2–5 utility values were found to have had the largest effect on ICER values.

In conclusion, the DSA, PSA and scenario analyses demonstrate the robustness of the base case results with respect to both the combined uncertainty of the model parameter inputs and the alternative plausible approaches and assumptions explored in the scenario analyses.

B.3.9 Subgroup analysis

No subgroups were evaluated in the economic analysis.

B.3.10 Validation