Daratumumab for treating relapsed and refractory multiple myeloma after a proteasome inhibitor and an immunomodulatory agent [ID933]

Transcription

1 Public observer slides Daratumumab for treating relapsed and refractory multiple myeloma after a proteasome inhibitor and an immunomodulatory agent [ID933] 1 st Appraisal Committee meeting Background and Clinical Effectiveness Committee B Lead team: Sumeet Gupta, John Cairns, Dani Preedy ERG: BMJ-TAG NICE technical team: Jessica Maloney, Ahmed Elsada 22 February 2017

2 Summary Company positions drug 4th line Uncertainties ERG states company s economic model not fit for purpose Evidence from single arm studies phase 2 and earlier ERG believes not appropriate to pool data from these 2 studies Matching adjusted indirect comparison very sensitive to number and choice of covariates Model does not adjust for subsequent treatments Innovation according to company First-in-class drug with a manageable safety profile 2

3 Multiple myeloma Bone marrow cancer of plasma cells Cancerous plasma cells produce large amounts of an abnormal antibody known as paraprotein, which supress the production of normal blood cells (white, red and platelets). Paraproteins cannot fight infection. Common symptoms Bone pain, fractures, anaemia, infections, hypercalcaemia Characterised by multiple relapses Incidence and survival 5,501 people diagnosed in the UK in 2014, that is, 2% of all cancers 45% diagnosed in people aged 75 and over (2012 to 2014) 5-year survival rate is approximately 50% 3

4 Factors associated with response to treatment and/or survival Response declines with: Each successive line for treatment Increasing age Cytogenetics Stage of disease International Staging System (ISS) based on serum beta2-microglobulin and albumin Revised criteria also include levels of serum lactate dehydrogenase and high-risk chromosomal abnormalities detected by interphase fluorescent in situ hybridization (FISH) 4

5 Pharmacological treatment options Proteasome inhibitors Immunomodulatory agents Histone deacetylase inhibitor Monoclonal antibodies Alkylating agents Bortezomib Thalidomide Daratumuab Bendamustine** Lenalidomide Panobinostat Cyclophosphamide Carfilzomib * Pomalidomide Elotuzumab* Melphalan * Undergoing NICE appraisal ** No marketing authorisation in the UK for this indication, but available through the CDF 5

6 Daratumumab (Darzalex ) Marketing authorisation Mechanism Administration Dose Adults with relapsed and refractory multiple myeloma whose prior therapy included: 1. a proteasome inhibitor (e.g. bortezomib) AND 2. an immunomodulatory agent (i.e. thalidomide analogues*) whose disease progressed on their last therapy. Regulators previously granted daratumumab orphan drug status (in 2013) (N.B. FDA limits daratumumab to 4 th line or later.) Human monoclonal antibody that binds to CD38 protein Intravenous infusion 16 mg/kg of body weight Weekly for weeks 1 to 8, every 2 weeks for weeks 9 to 24 and every 4 weeks from week 25 onwards Cost Acquisition cost (excluding VAT) for 100mg vial 1, for 400mg vial. Cost of a course of treatment (list price): 68,862 excluding administration costs, 74,531 including administration costs. * Such as lenalidomide and pomalidomide 6

7 Patient and professional feedback MM is a highly individual disease - important to have a range of treatment options/combinations acting through different mechanisms to give flexibility - significant unmet need Increasing survival and improving quality of life are important outcomes - people will have received a significant number of previous therapies and minimising side effects is a key concern Over time MM becomes resistant to treatment and this has a big impact on physical and emotional wellbeing - treatments which can give more certainty of a long remission and reduce the cycle of relapse and remission are particularly valued Carers emotional wellbeing, social life, work life are impacted and this can be greater for those caring for people with relapsed and refractory disease Treatment is administered similarly to other monoclonal antibody therapies - treatment by IV requires time in hospital and may require an overnight stay for the first infusion - severe reactions to treatment are uncommon 7

8 1 st line treatment Multiple myeloma treatment pathway Thalidomide + alkylating agents (e.g. melphalan, cyclophosphamide) (TA228) Bortezomib if thalidomide is contraindicated or cannot be tolerated (TA228) Bortezomib + thalidomide + dexamethasone (TA311) 2 nd line treatment Bortezomib initial treatment (TA129) Bortezomib retreatment (TA129) Lenalidomide + dexamethasone ongoing (ID667) 3 rd line treatment Lenalidomide (TA171) Panobinostat with bortezomib and dexamethasone (TA380) Daratumumab? 4 th line treatment Pomalidomide + dexamethasone (TA427) Panobinostat with bortezomib and dexamethasone Daratumumab? (Company choice) 5 th line treatment Other agents e.g. bendamustine Daratumumab?

9 Decision problem population Population narrower than scope and marketing authorisation NICE scope Company submission Company justification for difference People with relapsed and refractory multiple myeloma that has previously been treated with a proteasome inhibitor 1 and an immunomodulatory agent 2 and who have demonstrated disease progression on the last therapy. People who have received 3 or more prior therapies. Available trial data for daratumumab monotherapy Median lines of previous treatments: 5 in MMY2002, 4 in GEN501 Is placing daratumumab as 4 th line therapy appropriate? 1 Protease inhibitors e.g. bortezomib, carfilzomib 2 Immunomodulatory agents e.g. thalidomide, lenalidomide and pomalidomide 9

10 Decision problem comparators Lenalidomide with dexamethasone not included in submission NICE scope 1. Panobinostat with bortezomib and dexamethasone 2. Pomalidomide with dexamethasone 3. Bendamustine 4. Lenalidomide with dexamethasone Company justification for difference Company submission 1. Panobinostat with bortezomib and dexamethasone 2. Pomalidomide with dexamethasone 3. Bendamustine In current practice, LEN+DEX is used earlier in the treatment pathway (i.e. third-line). Trial data for LEN+DEX is based on use earlier in the treatment pathway compared with trial data for daratumumab ERG comments Agree that LEN+DEX not a comparator, used earlier than 4 th line Bendamustine not a comparator, used after 4 th line as a last treatment option Is LEN+DEX an appropriate comparator 4 th line? Is bendamustine an appropriate comparator 4 th line? 10

11 Decision problem outcomes Definition of time to treatment discontinuation unclear NICE scope Progression-free survival Overall survival Response rates Time to next treatment Adverse effects of treatment Health-related quality of life Company submission Progression-free survival Overall survival Response rates Time to next treatment Adverse effects of treatment Health-related quality of life Time to treatment discontinuation (used in company model to calculate cost of daratumumab treatment) 11

12 Clinical evidence 2 clinical trials, phase I/II non-controlled i.e. everyone treated MMY2002 (n=124) Phase II, randomised (dose) multicentre, open-label, 2-part study 3 prior lines of therapy OR Refractory to a PI and IMiD Part 1 Daratumumab 16mg/kg licence dose weekly for weeks 1 8 Daratumumab 8mg/kg every 4 weeks Part 2 Daratumumab 16mg/kg Primary outcome: overall response rate (OS and PFS secondary outcomes) GEN501 (n=72) Phase I/II, multicentre, open-label, 2-part study Relapsed or refractory to 2 prior lines of therapy Part 1 Dose escalation of daratumumab from mg/kg to 24 mg/kg Part 2 Daratumumab 8mg/kg Daratumumab 16mg/kg Primary outcome: safety (OS and PFS secondary outcomes) Are the patient populations in these trials the same as who would receive daratumumab in the NHS, given the marketing authorisation? 12

13 Pooled analysis of MMY2002 and GEN501 part 2 Post-hoc meta-analysis across MMY2002 and GEN501 part 2 pooling data from daratumumab 16mg/kg arms MMY2002 GEN501 13

14 Baseline characteristics Cytogenetic profile and ISS staging not assessed in GEN501 Study MMY2002 GEN501 Part 2 Pooled Sample size Age (years), median (range) 63.5 (31, 84) 64.0 (44, 76) 64.0 (31-84) Male, n% 52 (49) 27 (64) 78 (53) Time since initial diagnosis, median 4.8 (1.1, 23.8) 5.8 (0.8, 23.7) 5.1 ( ) years (range) Number of lines of prior therapy, 5 (2, 14) 4 (2, 12) 5 (2-14) median (range) 4 prior lines of therapy, n (%) 87 (82) 26 (62) 113 (76) % refractory to their last treatment 97% 76% - Cytogenetics and ISS staging Assessed Not assessed - ERG considers pooling data inappropriate as populations differ in terms of: % refractory to last treatment Prior lines of therapy Time since diagnosis Primary outcome Is it appropriate to pool data from the 2 trials? Key: IMiD, immunomodulatory agent; ISS, International Staging System; PI, proteasome inhibitor 14

15 Clinical evidence 2 key clinical trials phase I/II non-controlled Prior therapy, n (%) MMY (100) GEN (100) Pooled 148 (100) Bortezomib 105 (99) 42 (100) 147 (99) Carfilzomib* 53 (50) 8 (19) 61 (41) Prior IMiD, n (%) 106 (100) 40 (95) 146 (99) Lenalidomide 105 (99) 40 (95) 145 (98) Pomalidomide 67 (63) 15 (36) 82 (55) Thalidomide 47 (44) 19 (45) 66 (45) ERG comments: Pomalidomide toxicity could result in poorer performance on subsequent therapies Do prior therapies reflect clinical practice in the NHS? Should pomalidomide-naïve patients be considered separately? * Currently under appraisal and unavailable in NHS clinical practice 15

16 Kaplan-Meier plot PFS Pooled analysis daratumumab16 mg/kg MMY2002 GEN501 Part 2 Pooled n Number of events, n (%) 75 (70.8) 27 (64.3) 102 (68.9) Median PFS, months (95% CI) 3.7 (2.8, 4.6) 6.2 (4.2, 11.6) 4.0 (2.8, 5.6) Key: CI, confidence interval; PFS, progression-free survival. Notes Analysis based on 9 January 2015 data cut-off for MMY2002 and the 8mg/kg arm of GEN501 Part 2, 31 December 2015 data cut-off for the 16mg/kg arm of GEN501 Part 2. 16

17 Kaplan-Meier plot OS pooled analysis daratumumab16 mg/kg Number of events, n (%) Median OS, mts (95% CI) GEN501 Part Pooled MMY analysis 16mg/kg 16mg/kg 16mg/kg (n=106) (n=42) (n=148) 57 (53.8) 16 (38.1) 73 (49.3) 18.6 (13.7, NR) NR 20.1 (18.7, NR) (16.6, NR) Key: NR, not reached Key: CI, confidence interval; Dec, December; OS, overall survival. 17

18 Indirect treatment comparisons Company compared daratumumab to comparators indirectly given no randomised head-to-head evidence comparing daratumumab with any other treatment Treatment/ Method of Source Evidence Comparator comparison Daratumumab MMY2002/GEN501 Pooled IPD - POM+DEX MM-003 Aggregate MAIC data PANO+ PANORAMA 2 Aggregate MAIC BORT+DEX data Bendamustine International Myeloma Foundation IPD Multivariate regression Key: OS, overall survival; PFS, progression free survival; BORT/DEX, bortezomib plus dexamethasone; IPD, individual patient-level data; MAIC, matching-adjusted indirect comparison; POM+DEX, pomalidomide plus dexamethasone 18

19 Source of evidence forming MAIC MM-0003 GEN501 Part 2 MMY2002 Part 2 PANORAMA 2 19

20 Summary data for comparators in indirect comparisons PANORAMA-2 less heavily pre-treated than MMY2002 POM+DEX PANO+BORT+DEX Bendamustine Study MM-003 PANORAMA-2 Registry data (IMF) Analysis MAIC MAIC Multivariate regression Data Aggregate data Aggregate data Patient-level data Type of study Population Median no. prior tx Arm 1 RCT phase III, active controlled open label 2 cycles of bort+len, refractory to previous treatment Phase II, 2 stage, single-arm, open-label 2 previous treatments including immunomodulatory agent, refractory to bortezomib Retrospective observational chart review 3 previous treatments refractory to IMiD and PI 5 4 Not reported POM + low dose DEX (n=302) PANO+BORT+DEX (n=55 Eligible and at IMF sites (n=550) Arm 2 High-dose DEX (n=153) N/A (single-arm trial) N/A Cross over? Yes N/A - Subsequent therapies? Dexamethasone, cyclophosphamide, bortezomib, bendamustine Not reported in full publication - 20

21 MAIC process Daratumumab Pooled individual patient level data Comparator studylevel data Population from daratumumab pooled data that did NOT meet comparator trial inclusion criteria were excluded Inclusion criteria Inclusion criteria Pooled data weighted to match aggregate data Daratumumab population included in MAIC Mean values of baseline parameters (e.g. refractory status, no. of prior therapies) were adjusted to match comparator study 21

22 MAIC daratumumab vs POM+DEX n=84 Most important factors were matched to balance adjustment and reduction in sample size daratumumab trial population matched comparator inclusion/exclusion criteria, no one excluded n=148 Daratumumab Pooled individual patient level data n=148 Inclusion criteria Pooled data weighted to match aggregate data Pomalidomide + low dose dexamethasone aggregate data from MM-003 n.b. MM-003 excluded people with creatinine clearance <45 ml/min 11 characteristics matched (based on clinical expert opinion): Refractory status: 1. Refractory to lenalidomide (%) 2. Refractory to bortezomib (%) 3. Refractory to both (%) Number of prior therapies: 4. Mean number of prior regimens 5. >2 prior regimens (%) Creatinine clearance ml/min : 6. Creatinine clearance <30 (%) 7. Creatinine clearance (%) 8. Creatinine clearance 60 (%) Eastern Cooperative Oncology Group (ECOG) score: 9. ECOG 0 (%) 10. ECOG 1 (%) 11. ECOG 2 (%) 22

23 Company MAIC results daratumumab vs POM+DEX Number of matched characteristics N Net sample size 1 HR (95% CI) PFS Unadjusted (0.69, 1.12) 0.61 (0.46, 0.81) (0.50, 1.05) 0.56 (0.38, 0.83) (0.52, 1.08) 0.57 (0.34, 0.84) (0.58, 1.13) 0.59 (0.41, 0.86) (0.59, 1.13) 0.59 (0.41, 0.86) (0.57, 1.07) 0.55 (0.39, 0.78) (0.58, 1.05) 0.55 (0.39, 0.77) (0.60, 1.09) 0.57 (0.41, 0.81) (0.63, 1.09) 0.56 (0.41, 0.76) (0.64, 1.12) 0.60 (0.45, 0.81) (0.64, 1.11) 0.61 (0.45, 0.83) OS 1! Number of matched patients. The smaller the number, the poorer the overlap between studies, and the less stable the estimates. 23

24 MAIC daratumumab vs PANO+BORT+DEX Most important factors were matched to balance adjustment and reduction in sample size Daratumumab Pooled individual patient level data n=148 Panobinostat published aggregate data PANORAMA 2 23 people from daratumumab trial excluded. They did not meet PANORAMA 2 inclusion criteria, because they were NOT refractory to bort. n=125 Inclusion criteria Inclusion criteria Pooled data weighted to match aggregate data 5 characteristics matched (based on clinical expert opinion): Number of prior therapies: 1. Median number of prior regimens 2. >3 prior regimens (%) Eastern Cooperative Oncology Group (ECOG) score: 3. ECOG 0 (%) 4. ECOG 1 (%) 5. ECOG 2 (%) 24

25 Company MAIC results daratumumab vs PANO+BORT+DEX Population is subset of MMY2002/GEN501 pooled analysis excluding non-bort refractory according to PANORAMA 2 inclusion criteria n=125 Number of matched characteristics N Net sample size OS HR (95% CI) PFS (0.44, 1.30) 0.96 (0.60, 1.55) (0.48, 1.37) 0.98 (0.63, 1.53) (0.51, 1.38) 1.04 (0.69, 1.58) (0.51, 1.35) 1.08 (0.73, 1.59) (0.52, 1.37) 1.09 (0.74, 1.61) (0.57, 1.45) 1.19 (0.83, 1.72) 25

26 ERG comments on MAIC ERG considers most adjusted data set to be least biased for an unanchored comparison Limitations of unanchored MAIC No within-study randomisation means potential for confounding Analysis assumes that all effect modifiers and prognostic factors accounted for Causes bias and uncertainty in the validity of estimates Assumes that the relationship between covariates is the same as the intervention study Population modelled based on the comparator trial not the population for which IPD data are available Statistical error arising from unobserved variables should be quantified Should the MAIC analysis be based on important criteria or fully matched for all characteristics? 26

27 Cost-effectiveness 27

28 Company s model structure Partitioned survival model, 4 health states Preprogression On Treatment Pooled population from MMY2002/ GEN501 enter this state and receive: Preprogression Off Treatment 1. Daratumumab 2. POM+DEX 3. PANO+BORT+DEX 4. Bendamustine (not included by ERG) 15-year time horizon (lifetime), 1-week cycle length, 3.5% discount rate for costs and effects, NHS and PSS perspective on costs 28

29 Company s modelling OS, PFS and TTD Dependently fitted curves (dependent on other curve) assumes proportional hazards 1. Company fit parametric distributions to its pooled data for daratumumab and extrapolated beyond followup 2. Unadjusted survival curve for daratumumab estimated Illustrative only 3. Adjusted HRs from MAIC applied to daratumumab curve to estimate curves for POM+DEX and PANO+BORT+DEX End of trial follow-up Key: PFS, progression-free survival; ; PH, proportional hazards; MAIC, matching-adjusted indirect comparison; OS, overall survival; TTD, time to treatment discontinuation 29

30 Alternative approach to modelling OS, PFS and TTD not used by company Independently fitted curves (does not assume PH) 1. Fit a parametric distribution to MAIC-adjusted pooled data for daratumumab and extrapolate beyond followup Illustrative only 2. Fit and extrapolate separate parametric distributions to unadjusted curves from MM- 003 and PANORAMA2 to estimate curves for POM+DEX and PANO+BORT+DEX respectively End of trial follow-up Key: PFS, progression-free survival; PH, proportional hazards; MAIC, matching-adjusted indirect comparison; OS, overall survival; TTD, time to treatment discontinuation 30

31 Company s modelling of overall survival Company chose exponential distribution Key: PH, proportional hazards Number of patients at risk: 16 at 24 months 3 at 26 months ERG comments Exponential not appropriate when PH does not hold Problem aggravated as exponential assumes baseline hazard (i.e. daratumumab death rate) is constant over time ERG conducted exploratory analyses using Weibull distribution 31

32 Company s modelling of OS Exponential vs Weibull Company curves, curves fitted by ERG different Exponential and Weibull distributions fit data similarly, Weibull predicts smaller benefit Weibull Exponential Which distribution is more appropriate to model OS, the exponential or Weibull or neither? 32

33 Company s modelling of PFS Company chose log-normal distribution 33

34 Company s modelling of time to treatment discontinuation (TTD) Treatment Available data TTD estimate Daratumumab Patient-level Post hoc analysis of MMY2002/GEN50 pooled data POM+DEX Mean and median TTD (no TTD curve available) Calibrated (adjusted) TTD curve for daratumumab to match mean and median TTD observed in MM-003 PANO+BORT+ DEX None Treatment assumed to continue until progression, or until the Bendamustine None maximum number of treatment cycles reached ERG comments Estimating TTD lacks transparency and clarity, and the company s calibration approach is a black box Using these data carries potentially high risk PFS could be explored to derive treatment costs i.e. assuming patients stop treatment at disease progression Should company use TTD or PFS to estimate treatment costs? 34

35 Summary of key ERG comments on clinical effectiveness in the model Rests on the following points: 1. Confounding of OS caused by subsequent therapies received 2. Assuming proportional hazards for OS i.e. the approach to fitting curves (dependent vs independent) 3. Using MAIC-adjusted HRs 35

36 1. Effect of subsequent therapies Company made 2 important claims about the effectiveness of daratumumab: 1. Daratumumab will allow more patients to have subsequent therapy due to favourable safety profile (which allows recovery from cumulative toxicity) 2. Daratumumab will increase likelihood of patients benefiting from subsequent therapy due to novel mode of action ERG comments Despite 1 st claim, company assumed a smaller proportion of patients have treatment after daratumumab (55%) than the trial (72%) ERG refutes 2 nd claim based on expert opinion What evidence has the committee seen or heard that daratumumab would allow more patients to have subsequent treatment than its comparators? Would daratumumab improve the response to subsequent treatment? 36

37 1. Effect of subsequent therapies Most common anticancer therapies after daratumumab MMY2002 GEN501 Pooled part 2 Dexamethasone 60 (56.6) 26 (61.9) 86 (58.1) Pomalidomide 34 (32.1) 16 (38.1) 50 (33.8) Cyclophosphamide 33 (31.1) 14 (33.3) 47 (31.8) Carfilzomib 31 (29.2) 11 (26.2) 42 (28.4) Bortezomib 27 (25.5) 9 (21.4) 36 (24.3) Lenalidomide 8 (7.5) 15 (35.7) 23 (15.5) Do these treatments represent clinical practice in the NHS? 37

38 1. Effect of subsequent therapies ERG: daratumumab effect on OS highly confounded POM+DEX data also confounded, but to a much lesser extent because: A smaller proportion of patients had subsequent therapies (44% vs 72%) A much smaller proportion of patients had subsequent carfilzomib, lenalidomide and bortezomib (2%, 5% and 18% vs 28%, 15% and 24% respectively) Therapies not available in UK at this point in treatment pathway, and likely to increase OS considerably None of patients had subsequent POM+DEX; 31% of pts in daratumumab trials had it Large OS benefit of daratumumab compared with small, if any, PFS gain may be caused by subsequent therapies rather than daratumumab Is there sufficient evidence to suggest that carfilzomib and lenalidomide increase survival? If so, to what extent? 38

39 1. Effect of subsequent therapies Curves generated by ERG Which effect of subsequent therapies should the OS estimate reflect? All subsequent therapies (black line)? Some subsequent therapies? If so, which ones? No subsequent therapies (green line)? 39

40 2. Proportional hazards assumption ERG: Daratumumab vs POM+DEX violates proportional hazards based on company s MAIC-adjusted curves Kaplan Meier curves cross Proportional hazards or not? The log-log plot shows curves crossing Key: PH, proportional hazards 40

41 3. MAIC-adjusted hazard ratios All but 1 HR not statistically significant POM+DEX vs dara mab PANO+BORT+ DEX vs dara mab Company s base case: only important characteristics adjusted Population Pooled Number of characteristics adjusted 11 5 Sample size (effective sample size) (84) 125 (80) PFS HR (95% CI) 1.24 ( ) 0.92 ( ) OS HR (95% CI) 1.74 ( ) 1.19 ( ) ERG s preferred approach: all possible characteristics adjusted Population MMY2002 Number of characteristics adjusted Sample size (effective sample size) 1 93 (19) 84 (13) PFS HR (95% CI) 0.88 ( ) 1.18 ( ) OS HR (95% CI) 1.14 ( ) 1.64 ( ) ERG notes that Decision Support Unit (DSU) advises to include all prognostic variables and effect modifiers in weighting for MAIC 1 The number of matched patients. The smaller this number is, the poorer the overlap between studies is, and the less stable the estimates are. 41

42 3. MAIC-adjusted hazard ratios ERG: Number of characteristics adjusted for changes the effect of daratumumab on OS OS for daratumumab (adjusted) vs POM+DEX (unadjusted) OS for daratumumab (adjusted) vs PANO+BORT+DEX (unadjusted) DARA pooled 11 characteristics DARA MMY covariates DARA MMY characteristics POM + DEX PANO+BORT +DEX DARA pooled 5 characteristics Fully adjusted curve shows much less benefit for daratumumab vs POM+DEX than the partially adjusted curve Fully adjusted curve shows more benefit for daratumumab vs PANO+BORT+DEX than the partially adjusted curve 42

43 3. MAIC-adjusted hazard ratios ERG: Number of characteristics adjusted for changes the effect of daratumumab on PFS PFS for daratumumab (adjusted) vs POM+DEX (unadjusted) PFS for daratumumab (adjusted) vs PANO+BORT+DEX (unadjusted) DARA MMY characteristics POM + DEX DARA pooled 11 characteristics PANO+BORT +DEX DARA MMY covariates DARA pooled 5 characteristics The slightest shift in the curves is likely to have an impact on the costeffectiveness of daratumumab given the sensitivity of the model to PFS 43

44 3. MAIC-adjusted hazard ratios ERG exploratory analyses ERG explored using fully adjusted HRs in company s base case For OS, ERG also used baseline Weibull distribution instead of exponential Analysis carries other flaws in the company s analysis, but a step in the right direction Conclusion: model highly sensitive to adjustment of HRs Treatment Deterministic ICER (daratumumab vs comparator), list prices PFS OS Company base case ERG exploratory analysis Company base case ERG exploratory analysis Daratumumab POM+DEX 55,766 54,348 55, ,901 PANO+BORT+DEX 32, ,040 32,593 Dara mab dominates 44

45 Summary of the ERG s comments on Data source Modelling approach OS distribution MAICadjusted HRs clinical effectiveness in the model Company s preference ERG s preferred approach Rationale for ERG preference Pooled data MMY2002 data Allows adjusting the HR for more characteristics Dependently fitted curves (assumes PH) Independently fitted curves (does not assumes PH) The proportional hazards assumption does not hold Exponential Weibull Doesn t require constant hazards characteristic of the exponential distribution Adjustment for only the most important characteristics Adjustment for the maximum number of characteristics possible Decision Support Unit (DSU) recommendation 45

46 Utility values Quality of life in model depends on disease state (pre-progression and post progression), and adverse effects Utility values based on EQ-5D data collected in MM-003 (Palumbo et al.) Data valued using UK general population time trade-off values All grade 3 or 4 adverse events occurring in 5% of people in trials included by company Utility decrements based on published estimates + expert opinion sought by the company Pre-progression state 0.61; Progressive disease state 0.57 (same for all treatments) 46

47 Costs Drug costs: acquisition, administration and concomitant medication Disease management costs (same for all treatments) Adverse events costs Cost of treatments received after disease progression Company adjusted distribution of subsequent therapies received in pivotal trials to reflect available treatments in UK End of life costs ERG comments Not justified in a transparent way and not clinically plausible No option to have BSC Company assumed 55% of patients have treatment after daratumumab based on expert opinion instead of 72% in trials Treatments costs do not tally with effectiveness estimates from trials ERG explored changing resource use before and after disease progression to reflect feedback from clinical experts 47

48 Cost of subsequent therapies ERG preferred distribution of therapies in trials Clinical trials Modelled by company Expert opinion to ERG Dara mab POM+ DEX Dara mab POM+ DEX PANO+ B+DEX Dara mab POM+ DEX PANO+ B+DEX % patients 74% 44% 55% 39% 55% 90% 53% 73% DEX 58% 29% 24% 10% 14% 15% 21% 16% POM+DEX 31% 0% 0% 0% 0% 45% 0% 48% PANO+B+DEX 0% 0% 0% 0% 0% 25% 21% 0% Cyclo mide 32% 21% 13% 7% 9% 5% 11% 8% Carfilzomib 28% 2% 0% 0% 0% 0% 0% 0% Bortezomib 24% 18% 0% 6% 8% 0% 0% 0% Lenalidomide 15% 5% 0% 0% 0% 0% 0% 0% Melphalan 16% 8% 9% 0% 0% 0% 0% 0% Etoposide 10% 3% 6% 0% 0% 0% 0% 0% Bendamustine 14% 11% 0% 4% 6% 0% 0% 0% Thalidomide 7% 7% 0% 0% 0% 0% 0% 0% BSC 0% 0% 0% 0% 0% 10% 42% 32% What proportion of patients should be assumed to have subsequent therapy? Which subsequent therapy should patients have? 48

49 ERG general comment on the company s model The ERG does not consider the company s model to be fit for decision-making : Modelling approach flawed i.e. curve fitting, choice of parametric distributions, and HR adjustment OS estimates highly confounded by subsequent therapies received in daratumumab trials Lack of robust internal quality assurance process on company's model evidenced by number of errors found In ERG s opinion, company s model and data analysis need further internal consistency and quality checks 49

50 Scenario ERG exploratory analyses All use the MMY2002 population Rationale 1 Using fully adjusted HRs for PFS DSU recommendation 2 Deriving treatment costs from PFS curves i.e. assuming treatment stops at disease progression 3 Using fully adjusted HRs for OS, and a baseline Weibull distribution for daratumumab TTD lacks transparency and clarity DSU recommendation 4 Changing resource use in health states Feedback from experts 5 Modelling subsequent therapies received in daratumumab and POM+DEX trials (as far as possible) 6 Modelling subsequent therapies in the NHS based on expert opinion 7 Assuming no effect for daratumumab on PFS and OS (i.e. HR=1) compared with comparators Align treatments costs with effectiveness estimates from trials Reflect clinical practice None of the HRs is statistically significant 8 Removing the utility decrements for adverse events Remove double-counting of the impact of AEs 50

51 Innovation Treatment options for heavily pre-treated and refractory population are limited. Daratumumab is a first-in-class drug Manageable safety profile Equality and diversity Multiple myeloma is more common in men than in women and the incidence is also reported to be higher in people of African family origin 51

52 End of life considerations (1) NICE criterion Short life expectancy (<24 months) Company assessment Median life expectancy: (<24 months) ERG assessment Undiscounted total life-years for daratumumab compared with each treatment: Company analysis ERG exploratory analysis (adjusted HRs) POM+DEX 17 months 22 months PANO+BORT+ DEX 24 months 16 months 52

53 NICE criterion 3-month life extension End of life considerations (2) Company assessment Mean OS estimates: Pano+bort+dex: gain 4.7 months Pom+dex: gain 12.9 months ERG assessment Undiscounted months gained for daratumumab compared with each treatment: Company analysis ERG exploratory analysis (adjusted HRs) POM+DEX 9 months 3 months PANO+BORT+ DEX 2 months 9 months 53

54 Issues Overall suitability of the company s model for decision-making The lack of RCT data High risk of bias Time to event data were from single-arm studies No clinical effectiveness estimates from head-to-head studies Position of daratumumab in treatment pathway The source of data for daratumumab: pooled data vs data from MMY2002 The confounding effect of subsequent therapies Subgroups Pomalidomide-naïve Subgroups receiving specific subsequent therapies or no therapies The modelling approach: dependently vs independently fitted curves (i.e. PH vs no PH) MAIC-adjusted HRs: only important characteristics matched vs maximum number of characteristics Estimating treatment costs: TTD vs PFS Estimating treatment costs for subsequent therapies 54

55 CDF Recommendation Decision Pathway Proceed down if answer to each question is yes Starting point: drug not recommended for routine use 1. Why is drug not recommended? Is it due to clinical uncertainty? 2. Does drug have plausible potential to be cost-effective at the current price, taking into account end of life criteria? 3. Could data collection reduce uncertainty 4. Will ongoing studies provide useful data? and Recommend enter CDF 5. Is CDF data collection feasible? Define the nature of clinical uncertainty and the level of it. Indicate research question, required analyses, and number of patients in NHS in England needed to collect data 55