Survival Extrapolation Research Articles

EX4 Bayesian MULTI-Parameter Evidence Synthesis to Improve LONG TERM Survival Extrapolation with EARLY Follow up DATA from Nivolumab NSCLC Studies

Immuno-oncology therapies can be associated with responses that are delayed but durable and related to long-term survival. This may lead to complex hazard functions which standard parametric models fail to accurately model with often only early trial data available for HTA assessments. We used Bayesian multi-parameter evidence synthesis (B-MPES), a method which enables incorporation of external evidence, to provide long-term survival extrapolations in comparison to standard parametric models. We assessed the performance of B-MPES using databases with minimum follow up of 1- to 5-years from trials evaluating nivolumab versus docetaxel in patients with pretreated metastatic squamous (CheckMate-017) and non-squamous (CheckMate-057) NSCLC. A natural cubic spline model was used to combine overall survival data from each trial with Surveillance, Epidemiology, and End Results (SEER) cancer registry data. Cumulative hazard plots were examined to identify spline knot locations. Performance of the survival extrapolations with 1-, 2-, and 3-year dataset-based B-MPES models were compared with the validated standard parametric model (SPM) for the 5-year dataset using restricted mean survival time (RMST). Additional comparisons against best fit SPMs with earlier data will also be presented. The estimated RMST (95% CI) in months for nivolumab from the 2-, 3-, and 5-year datasets of CheckMate-017 over a 20-year horizon were 24.0 (19.0-29.5), 25.2 (20.7-30.7), and 30.5 (23.0-35.9), respectively, with B-MPES models and 30.5 (21.4-43.6) with the validated 5-year SPM. For CheckMate-057, the estimated 20-year RMST from the 2-, 3-, and 5-year datasets were 22.4 (19.0-26.1), 32.8 (28.2-37.9), and 35.5 months (28.6-44.4), respectively, with B-MPES and 28.6 months (23.5-34.5) with the 5-year validated SPM. We demonstrate that incorporating data from external data sources using B-MPES, yields comparable results to 5-year SPMs using earlier datasets. For CheckMate-057, B-MPES fitted to 3- and 5-year datasets yields more favorable survival extrapolations than the 5-year SPM.

PCN258 Using Mixture Cure Models to Improve Survival Extrapolations from EARLY Follow-up DATA for Nivolumab in NSCLC

Selecting appropriate parametric models to extrapolate long-term survival based on limited follow up trial data may improve the reliability of HTA evidence. Using data from two phase 3 trials in 2L NSCLC, we assess whether mixture cure models (MCMs) improve survival extrapolation relative to standard methods. The analyses were based on datasets with minimum of 5 years of follow up from trials comparing nivolumab versus docetaxel in patients with pretreated metastatic squamous (CheckMate-017) and non-squamous (CheckMate-057) NSCLC. MCMs assume a fraction of patients are cured by treatment, with uncured and cured patients subject to separate survival curves. We estimated MCMs on 1-3 year databases and assessed the survival extrapolations against the validated standard parametric models (SPM) using the 5-year dataset. Validation of models was facilitated by comparing the restricted mean survival time (RMST) over 20-years. Additional comparisons against SPMs from earlier datasets will be presented. For CheckMate-017, MCMs fitted to the 1-year (PFS) and 3-year (OS) datasets were the earliest datasets modeled to yield results comparable to the 5-year validated SPM. For CheckMate-057, the 2-year (PFS) and 3-year (OS) datasets were the earliest datasets modeled to yield results comparable to the 5-year validated SPM. For CheckMate-017, the 20-year RMST estimate for PFS from the 1-year data was 25.5 vs 22.9 months for the 5-year SPM, and the 3-year OS MCM was 30.2 vs 30.5 for the 5-year SPM. For CheckMate-057, the 20-year RMST for the 2-year PFS MCM was 23.6 vs 19.0 for the 5-year SPM, and the 3-year OS MCM was 27.5 vs 28.6 for the 5-year SPM We demonstrate that the use of MCMs for modeling survival with early follow-up in NSCLC patients could anticipate validated standard survival extrapolations with extended follow-up.

PCN265 Assessment of Different Methods for the Extrapolation of Survival Statistics for Nivolumab in Metastatic Melanoma: Implications for Cost-Effectiveness in HTA

Extrapolation of immature overall survival (OS) or progression-free survival (PFS) trial data using standard parametric (SP) models based on prior distributions (e.g. Exponential, Weibull, Gompertz) is commonly used for economic modelling in HTA. Due to complex hazard functions, SP models may produce a poor fit for OS and PFS for immuno-oncology (I-O) treatments. This project aims to compare SP models with cubic spline (CS) methods, to assess the impact of extrapolation methods on estimated cost-effectiveness of nivolumab compared to dacarbazine in metastatic melanoma. OS and PFS data were extracted from digitised Kaplan-Meier curves reported in the CheckMate 066 trial. SP and CS models were fitted to two data cut-offs at 16 and 52 months to assess the effect of different follow-up periods on model choice. Incremental cost-effectiveness ratios (ICERs) per QALY gained were estimated using a partitioned survival model with a lifetime horizon. ICERs for nivolumab vs. dacarbazine were similar for SP and CS models fitted to the 16-month data cut-off (£23,244, £24,026). Based on the 52-month cut-off, nivolumab was more cost-effective based on SP fit compared to CS (£22,269 vs. £34,850). CS models were a better fit to the KM data based on visual inspection. The choice of distribution for SP model of nivolumab 16-month OS presented a wide range of ICER estimates (£13,753-52,466). The Gompertz SP model often presented the lowest AIC, but also resulted in a significantly different predicted survival (and thus lower ICER) compared to other distributions. The choice of statistical fit in SP models had a large impact on lifetime ICER with short-term trial data. Both models provided a good fit for the plateau in OS using the long data cut-off. CS models provide a robust fit and less uncertainty for HTA in the absence of long-term survival endpoints for I-O treatments.

EX2 Structural Uncertainty in Survival Extrapolation: Exploring the IMPACT of FOUR MODEL Averaging Methods and Adjusting for DATA Maturity

EX2 Structural Uncertainty in Survival Extrapolation: Exploring the IMPACT of FOUR MODEL Averaging Methods and Adjusting for DATA Maturity

ON4 Heterogeneity in Survival with Immuno-Oncologic Therapy and Its Implications for Survival Extrapolations: A Case Study in Untreated Metastatic Melanoma

Short clinical trial follow-up periods at the time of submission and heterogeneity in outcomes often introduce uncertainty in the health technology assessment of immuno-oncologic (IO) therapies. To address this, some agencies prefer to use conservative methods for survival extrapolation. To evaluate the predictive accuracy of various survival extrapolation methods using data from CheckMate 067, a phase III trial of nivolumab plus ipilimumab (NIVO+IPI) vs either monotherapy in untreated metastatic melanoma. Six different extrapolation models were independently fitted to overall survival (OS) data for the three arms of CheckMate 067 using successive database locks (DBL) at: 28, 40, 52 and 60 months. NICE DSU TSD 14 informed model selection. 60-month follow-up from CheckMate 067, as well as external data for ipilimumab, were used to evaluate the predictive accuracy of the survival models. Extrapolated 20-year mean survival times (20-YR MST) were also compared with a 5% discount rate applied. For the 28-month DBL, standard parametric models, piecewise models and splines underestimated 60-month survival (range -3.8% to -11.7% across methods/arms). Mixture cure models (MCM), parametric mixture models (PMM) and response-based landmark models (RBLM) provided more accurate estimates (range -3.2% to +2.0% across methods/arms). MCM, PMM and RBLM produced closer estimates of 7-year and 10-year OS when compared to the external data for ipilimumab than other methods. 20-YR MSTs were higher for MCM, PMM and RBLM (NIVO+IPI: 6.02-7.02 years) than those for other models (NIVO+IPI: 4.56-5.62 years). Estimates of 20-YR MST for MCM, PMM and RBLM were more consistent across the successive database locks than with other methods. MCM, PMM and RBLM provided accurate and consistent estimates of OS across a range of follow-up periods. Extrapolation models that fail to account for heterogeneity in survival may result in biased assessments of the survival benefit of IO therapies for melanoma.

ON2 Assessing the Performance of Landmark Response Analysis to Improve Overall Survival Extrapolation in Nivolumab NSCLC Trials

ON2 Assessing the Performance of Landmark Response Analysis to Improve Overall Survival Extrapolation in Nivolumab NSCLC Trials

PCN136 Cost-Effectiveness Analysis of Dacomitinib Versus Gefitinib for the First-line Treatment of Locally Advanced or Metastatic Non-Small Cell Lung Cancer with Epidermal Growth Factor Receptor (EGFR)-Activating Mutations in Portugal

To evaluate the cost-effectiveness of dacomitinib versus gefitinib for the first-line treatment of locally advanced or metastatic non-small cell lung cancer with epidermal growth factor receptor (EGFR)-activating mutations in Portugal. The cost-effectiveness analysis was based on a partitioned survival model using data from ARCHER 1050 trial, a head-to-head phase III randomized controlled trial of dacomitinib versus gefitinib. The model estimated patients’ pathway through progression free survival, post-progression and death health states. Utility weights varied by treatment line and were derived from ARCHER 1050 using EQ-5D-3L for first line treatment and based on the literature for the remaining treatment lines and best supportive care. Portuguese-specific resource use was based on a panel of clinical experts and on Portuguese diagnosis-related group microdata. National legislation and official drug cost databases were the main sources for unit costs. The analysis was conducted from a payers’ perspective, assuming a lifetime horizon, and a 5% annual discount rate for both costs and effects. Deterministic and probabilistic sensitivity analyses were conducted to assess the robustness of the model and its results. Dacomitinib increased average life expectancy enabling a discounted gain of 0.45 life years (LY) or 0.29 quality adjusted life years (QALY). Economic analysis showed overall higher costs with gefitinib (10,912€), mainly due to drug costs in both first and subsequent treatment lines. Dacomitinib was a dominant option when compared to gefitinib. Deterministic sensitivity analyses showed that results were robust in most evaluated scenarios. Despite slightly sensitive to parametric extrapolation of progression-free survival, the dominance relationship was still observed. The cost-effectiveness analysis of dacomitinib for the first-line treatment of locally advanced or metastatic non-small cell lung cancer with EGFR-activating mutations showed that dacomitinib was a dominant option when compared to gefitinib in the Portuguese setting.

ON1 Comparison of Overall Survival (OS) Extrapolations of Immune-Checkpoint Inhibitors (ICI) in Efficiency Opinions of French 'Haute Autorite de Sante' (HAS) with Updated OS data: Were the Projections Accurate?

Given the limited follow-up available for ICI at the time of HAS submission, OS extrapolation is required in cost-effectiveness models. The objective of this study was to assess retrospectively the accuracy of OS projections from French efficiency opinions (EO) using extended follow-up of randomized clinical trials (RCT) of ICI. Analysis was restricted to EOs available until May 15th 2020 meeting the following inclusion criteria: 1) ICI treatment for advanced cancer, 2) additional follow-up ≥ 6 months of the RCT available in the literature. In the EOs, drug indication, duration of follow-up, type of model, survival rates at different landmarks, and maturity of the data (number of deaths) were collected. In the publications of RCTs with extended follow-up, duration of follow-up and the most robust long-term OS rate were gathered. We calculated the relative difference between OS rates from each source. Out of the 121 EOs published, 11 EOs met the inclusion criteria. Half of the ICI were indicated in non-small-cell lung cancer (N=6). Mean additional follow-up was 28.2 months (min: 6; max: 52). Among the EOs, 55% used extrapolation methods from randomization while 45% used piecewise models combining Kaplan-Meier data and extrapolation. The mean relative difference of OS rate between each source was -13%. The OS rate was underestimated in 73% of the EOs. For the ICI with an additional follow-up of minimum 3 years (N=4), the mean relative difference was -25%. However, no trend between maturity of data mentioned in EO and the accuracy in OS projection was observed. OS projections were underestimated in most of the ICI EOs, with a pronounced underestimation when additional follow-up of minimum 3 years was available. Based on the available data at the time of HAS submission, the long-term benefit of ICI does not seem to be fully captured in the OS extrapolations.

PDG20 Cost-effectiveness of Nivolumab in Patients with Pre-treated Advanced Renal Cell Carcinoma (aRCC) in the United States: Impact of >5 Years of Follow-up Data from Checkmate 025

This study compares cost-effectiveness (CE) results when using data from the initial and latest database locks (DBLs) of CheckMate 025 (NCT01668784), a randomized phase 3 trial investigating nivolumab versus everolimus in pre-treated aRCC. The minimum follow-up was 14 months (initial) and 64 months (latest) in these DBLs, respectively. We developed a 3-state (progression-free, progressive disease and death) partitioned survival model to evaluate the CE of nivolumab versus everolimus from a United States payer perspective, with a 25-year time horizon and 3% discounting for costs (2020 US$) and outcomes. Key trial data inputs from the DBLs were progression-free survival (PFS), overall survival (OS), time-to-treatment discontinuation (TTD), treatment response, patient utilities, and subsequent systemic treatment. For comparison, only the inputs derived from the trial were changed in this analysis. Using longer term data resulted in a modest reduction in the incremental cost-utility ratio (ICUR) for nivolumab versus everolimus ($70,490/QALY [incremental cost-effectiveness ratio (ICER): $54,240/LY] versus $72,389/QALY [$55,946/LY] using the initial DBL). The initial DBL had the same (best-fitting) functional forms for survival extrapolations as the latest DBL but underestimated incremental LY and QALY gains with nivolumab by 11.4% and 11.0%, respectively. Based on OS extrapolations from the initial DBL, total LYs for nivolumab and everolimus were 3.1% and 0.6% lower, respectively. Changes in incremental costs (+8.6% with latest DBL) were mainly driven by differences in the use of subsequent treatment and the types of agents given between the two DBLs. Health state utility values, treatment response, and TTD curves between DBLs had limited ICUR influence. The initial DBL conservatively underestimated the incremental lifetime survival benefit for nivolumab versus everolimus when compared with estimates using the latest DBL from CheckMate 025. Higher total incremental costs observed with longer follow-up were offset by increased LY and QALY gains between DBLs.

NM1 A Comparison on Different Network-Meta Analysis (NMA) Models Using Randomized Clinical Trials (RCTS) in Transplant-Ineligible (TIE) NEWLY Diagnosed Multiple Myeloma (NDMM)

An NMA survival extrapolation decision-framework based on clinical plausibility is proposed. It suggests mixture-cure models in case cure is clinically realistic. In case of no cure, parametric mixture (PM) models are proposed for subgroups with long-term survival, and piecewise (PW), splines (S) and fractional polynomial (FP) models are suggested for subsets with fluctuating hazards. Otherwise, standard parametric approaches are proposed. Following the framework, a parametric NMA (PNMA) would be most clinically plausible in TIE NDMM since cure, and subgroups with long-term survival and fluctuating hazards are not anticipated. It was aimed to compare estimated survival using different NMA methods. The network of evidence included n=5 RCTs on n=3 treatments (MP, MPT-T, MPR-R) in TIE NDMM. Published Kaplan-Meiers were used to extract individual-patient data. The proportional-hazard assumption was violated, and thus a PNMA was conducted. The PNMA has been compared to PW, S, PM, and FP models. Mixture- and non-mixture cure models were excluded since cure was not clinically realistic. Only the Weibull distribution was used to extrapolate survival. The overall model fit was assessed using the leave-one-out-information-criteria (LOOIC). The PNMA demonstrated lowest LOOIC [6588.62], compared to the other models (second-lowest: FP [6588.89]). PNMA and FP models predicted survival of respectively 4.47 and 4.67 years for MP, 5.50 and 5.50 years for MPT-T, and 4.70 and 4.45 years for MPR-R. Incremental survival of MP against MPT-T and MPR-R was 1.04 and 0.23 years for PNMA and 0.83 and 0.22 years for FP. Incremental survival between PNMA and FP was 0.20 and 0.45 years for MP versus MPT-T and MPR-R, respectively. The framework demonstrates to work. Estimated survival varied per model, which impacts health economic outcomes such as the incremental cost-effectiveness ratio (ICER), relevant for medical decision-making. Conducting NMA methods should be tailored to the data and take clinical plausibility into account.

Economic evaluation of nivolumab combined with ipilimumab in the first-line treatment of advanced melanoma in Japan

Aims The objective of this study was to evaluate the cost-effectiveness of nivolumab in combination with ipilimumab (nivo + ipi) compared to current therapeutic alternatives in first-line treatment of patients with advanced melanoma from the Japanese national healthcare payer perspective using 48-month survival data from the CheckMate 067 Phase III trial. Materials and methods A three-state partitioned survival model was developed from projections of overall survival (OS) and progression-free survival (PFS) to estimate accrued quality-adjusted survival and costs over a 30-year time horizon. The analysis included nivo + ipi, nivolumab, and ipilimumab monotherapies (the three treatments included in CheckMate 067). Drug acquisition, administration, disease management, subsequent therapy, and adverse event (AE) costs were obtained via published sources and expert input (solicited via Delphi panel). AE frequencies were collected from the Checkmate 067 trial. Utility weights were estimated from the Checkmate 067 trial, based on Japanese tariffs. Results were presented as incremental cost-utility ratios (ICURs, cost per quality-adjusted life-year (QALY)). Results Nivo + ipi had the greatest estimated survival among the three competing treatments, followed by nivolumab monotherapy accruing the second greatest survival. The incremental cost-effectiveness of nivo + ipi was ¥778,000 per QALY vs. nivolumab and ¥1,584,000 per QALY vs. ipilimumab. The results indicate that nivo + ipi is cost-effective in Japan when compared to a threshold of ¥7,500,000 per QALY. This finding was found to be generally robust to sensitivity and scenario analyses. Limitations Limitations include uncertainty in long-term survival extrapolations and lack of Japan-specific clinical data. Conclusions This analysis indicates that adding ipilimumab to nivolumab therapy represents a cost-effective new treatment option for patients with unresectable malignant melanoma in Japan.

Partitioned Survival and State Transition Models for Healthcare Decision Making in Oncology: Where Are We Now?

Partitioned survival models (PSMs) are routinely used to inform reimbursement decisions for oncology drugs. We discuss the appropriateness of PSMs compared to the most common alternative, state transition models (STMs). In 2017, we published a National Institute for Health and Care Excellence (NICE) Technical Support Document (TSD 19) describing and critically reviewing PSMs. This article summarizes findings from TSD 19, reviews new evidence comparing PSMs and STMs, and reviews recent NICE appraisals to understand current practice. PSMs evaluate state membership differently from STMs and do not include a structural link between intermediate clinical endpoints (eg, disease progression) and survival. PSMs directly consider clinical trial endpoints and can be developed without access to individual patient data, but limit the scope for sensitivity analyses to explore clinical uncertainties in the extrapolation period. STMs facilitate these sensitivity analyses but require development of robust survival models for individual health-state transitions. Recent work has shown PSMs and STMs can produce substantively different survival extrapolations and that extrapolations from STMs are heavily influenced by specification of the underlying survival models. Recent NICE appraisals have not generally included both model types, reviewed individual clinical event data, or scrutinized life-years accrued in individual health states. The credibility of survival predictions from PSMs and STMs, including life-years accrued in individual health states, should be assessed using trial data on individual clinical events, external data, and expert opinion. STMs should be used alongside PSMs to support assessment of clinical uncertainties in the extrapolation period, such as uncertainty in post-progression survival.

S0007 Palliative Endoscopic Biliary Radiofrequency Ablation and Percutaneous Biliary Radiofrequency Ablation as Therapies for Patients With Unresectable Cholangiocarcinoma: A Meta-Analysis on Overall Survival Outcomes

INTRODUCTION: Cholangiocarcinoma (CCA) has an extremely poor prognosis with literature demonstrating 5-year survival rates between 2 and 25% and median survival between 3 and 6 months for unresectable disease. Up to 80% of patients presenting with CCA have unresectable disease and these patients are left with palliative treatment options. Mainstay palliative therapy (apart from systemic chemotherapy) includes percutaneous or endoscopic stenting, however these treatment options have a relatively poor survival benefit. Radiofrequency ablation is a relatively new technology that delivers thermal energy at the tumor site resulting in locoregional coagulative necrosis. This meta-analysis aims to compare survival data for patients who underwent endoscopic radiofrequency ablation (ERFA) or percutaneous radiofrequency ablation (PRFA) as a palliative therapy for unresectable CCA. METHODS: A PubMed search was conducted for manuscripts and abstracts that analyzed survival outcomes in patients undergoing either ERFA or PRFA for the treatment of unresectable CCA. Studies were included in the meta-analysis only if extrapolation of CCA specific survival was made available. Studies analyzing post-surgical disease or recurrent lesions were excluded. A Kaplan Meier curve was generated from pooled survival data and a Forest plot compiled for comparison of mean survival between studies. RESULTS: There were 23 total studies that met our criteria and included in quantitative analysis. 14 studies analyzed ERFA and the survival outcomes of 213 patients. Additionally, 9 studies analyzed 114 patients and their survival outcomes after undergoing PRFA. One study included 30 patients undergoing either PRFA or ERFA. The mean survival of the ERFA group was 12.1 ± 4.4 months and mean survival of the PRFA group was 14.3 ± 7.5 months. The combined mean survival of all 357 patients undergoing palliative ERFA or PRFA was 12.8 ± 5.4 months. The median survival of the ERFA group was 12.0 months and the median survival of the PRFA group was 10.8 months. The overall median survival of all patients undergoing biliary RFA was 10.8 months. CONCLUSION: Biliary radiofrequency ablation seems to be reasonable for palliation in those with previously untreated, unresectable CCA with evidence of increased mean and median survivals relative to published data. However further studies are required to draw meaningful conclusions regarding survival benefit.Figure 1.: Forest Plot of mean survival of patients undergoing biliary RFA (ERFA and PRFA) for treatment of unresectable cholangiocarcinoma.Figure 2.: Kaplan-Meier survival curve of patients undergoing biliary RFA (ERFA and PRFA) for treatment of unresectable cholangiocarcinoma.

Cost-Effectiveness of Initial Versus Delayed Lanreotide for Treatment of Metastatic Enteropancreatic Neuroendocrine Tumors.

The Controlled Study of Lanreotide Antiproliferative Response in Neuroendocrine Tumors (CLARINET) trial showed prolonged progression-free survival in patients initially treated with lanreotide versus placebo. We evaluated the cost-effectiveness of upfront lanreotide versus active surveillance with lanreotide administered after progression in patients with metastatic enteropancreatic neuroendocrine tumors (NETs), both of which are treatment options recommended in NCCN Clinical Practice Guidelines in Oncology for Neuroendocrine and Adrenal Tumors. We developed a Markov model calibrated to the CLARINET trial and its extension. We based the active surveillance strategy on the CLARINET placebo arm. We calculated incremental cost-effectiveness ratios (ICERs) in dollars per quality-adjusted life-year (QALY). We modeled lanreotide's cost at $7,638 per 120 mg (average sales price plus 6%), used published utilities (stable disease, 0.77; progressed disease, 0.61), adopted a healthcare sector perspective and lifetime time horizon, and discounted costs and benefits at 3% annually. We examined sensitivity to survival extrapolation and modeled octreotide long-acting release (LAR) ($6,183 per 30 mg). We conducted one-way, multiway, and probabilistic sensitivity analyses. Upfront lanreotide led to 5.21 QALYs and a cost of $804,600. Active surveillance followed by lanreotide after progression led to 4.84 QALYs and a cost of $590,200, giving an ICER of $578,500/QALY gained. Reducing lanreotide's price by 95% (to $370) or 85% (to $1,128) per 120 mg would allow upfront lanreotide to reach ICERs of $100,000/QALY or $150,000/QALY. Across a range of survival curve extrapolation scenarios, pricing lanreotide at $370 to $4,000 or $1,130 to $5,600 per 120 mg would reach ICERs of $100,000/QALY or $150,000/QALY, respectively. Our findings were robust to extensive sensitivity analyses. The ICER modeling octreotide LAR is $482,700/QALY gained. At its current price, lanreotide is not cost-effective as initial therapy for patients with metastatic enteropancreatic NETs and should be reserved for postprogression treatment. To be cost-effective as initial therapy, the price of lanreotide would need to be lowered by 48% to 95% or 27% to 86% to reach ICERs of $100,000/QALY or $150,00/QALY, respectively.

Current challenges for assessing the long-term clinical benefit of cancer immunotherapy: a multi-stakeholder perspective

Immuno-oncologics (IOs) differ from chemotherapies as they prime the patient’s immune system to attack the tumor, rather than directly destroying cancer cells. The IO mechanism of action leads to durable...

Cost-effectiveness and financial risks associated with immune checkpoint inhibitor therapy.

The reimbursement of immune checkpoint inhibitors is challenging. Funding these technologies involves the careful balance between awarding innovation and ensuring affordability as increases in drug spending compete directly with other health care and social expenditure. This narrative review examines the recommendations of 2 health technology assessment agencies-the Australian Pharmaceutical Benefits Advisory Committee and the British National Institute of Clinical Excellence-to determine the factors that contribute to the approval and rejection of immune checkpoint inhibitors as well as the use of manage entry schemes and risk management strategies to control expenditure. Reimbursement decisions from 6 immune checkpoint inhibitor drugs (ipilimumab, pembrolizumab, nivolumab, durvalumab, atezolizumab, avelumab) covering 10 different cancers were examined. The extrapolation of survival beyond the clinical trial and lack of head-to-head evidence are some of the main issues relating to cost effectiveness. Payers managed financial risks using different mechanisms such as risk share agreements and financial caps. This review of the reimbursement decisions and subsequent financial impact in Australia and the UK suggests budgets for immune checkpoint inhibitor therapy have been well managed so far. Through risk agreements and managed entry programmes, the example of immune checkpoint inhibitor therapies illustrates that industry and payers can effectively collaborate to ensure that innovative, but expensive, drugs can be made readily available to patients.

The cost effectiveness of pembrolizumab versus chemotherapy or atezolizumab as second-line therapy for advanced urothelial carcinoma in the United States

Aims: Pembrolizumab demonstrated significantly prolonged overall survival (OS) vs. chemotherapy in the Phase III KEYNOTE-045 trial, and is approved in the US for the treatment of patients with locally advanced or metastatic urothelial carcinoma (mUC) who progressed after platinum-based chemotherapy. Using longer follow-up and individual patient-data from KEYNOTE-045, this study evaluates the cost-effectiveness of pembrolizumab vs. chemotherapy or atezolizumab from a US payer perspective.Materials and methods: A partitioned-survival model was developed over a 20-year time horizon. Progression-free survival (PFS) and OS for pembrolizumab and chemotherapy were extrapolated using a piecewise modelling approach, where patient-level data from KEYNOTE-045 were used for the initial period followed by parametric distributions. OS of atezolizumab was estimated by indirect treatment comparisons based on KEYNOTE-045 and IMvigor211. Different scenarios were explored in the absence of indirect comparisons on PFS and time-on-treatment (ToT) between pembrolizumab and atezolizumab. Drug acquisition/administration, disease management, adverse events, and terminal care costs were considered.Results: Compared with chemotherapy, pembrolizumab resulted in a mean gain of 1.33 life-years and 1.14 quality-adjusted life-years (QALYs) and an incremental cost of $106,299, yielding an incremental cost-effectiveness ratio of $93,481/QALY gained. Pembrolizumab dominated atezolizumab in extending patients’ life by 0.89 years and 0.76 QALYs, while reducing costs by $26,458. Key drivers of cost-effectiveness included survival extrapolation, OS hazard ratio of pembrolizumab vs. atezolizumab, and time horizon. Pembrolizumab had a 66% and 100% probability of being cost-effective vs. chemotherapy and atezolizumab, respectively, at a $100,000 willingness-to-pay threshold.Limitations and conclusions: Uncertainties remain with extrapolated PFS and OS for pembrolizumab, OS indirect comparison, and ToT for atezolizumab. Despite these limitations, the model used robust methods to estimate key clinical endpoints with patient-level data from longer follow-up of KEYNOTE-045. Pembrolizumab dominates atezolizumab and is very likely cost-effective vs. chemotherapy in 2 L mUC at a $100,000 willingness-to-pay threshold.

Extrapolation of Survival Benefits in Patients with Transthyretin Amyloid Cardiomyopathy Receiving Tafamidis: Analysis of the Tafamidis in Transthyretin Cardiomyopathy Clinical Trial.

IntroductionIn the Tafamidis in Transthyretin Cardiomyopathy Clinical Trial (ATTR-ACT; ClinicalTrials.gov number NCT01994889), tafamidis reduced the risk of all-cause mortality in patients with transthyretin amyloid cardiomyopathy (ATTR-CM) by 30% versus placebo. Median overall survival was not achieved in either treatment arm (57.1 and 70.5% of patients in the placebo and tafamidis groups, respectively, survived at 30 months), limiting assessment of the potential survival benefits of treatment.MethodsA survival extrapolation analysis was conducted following technical support guidelines from the National Institute for Health and Care Excellence. Multiple models (i.e., exponential, Weibull, gamma, log-logistic, log-normal, Gompertz, generalized gamma, and generalized F) were applied to systematically fit different candidate curves to existing patient-level data from the 30-month treatment period in ATTR-ACT. The relative goodness-of-fit for each candidate curve was then tested by Akaike’s and Bayesian information criteria to select a single model that was fitted to the placebo and pooled tafamidis treatment arms.ResultsA gamma distribution was selected as best fitting model and fitted to both treatment arms. The resulting estimated median overall survival was 35.16 months for placebo and 52.64 months for tafamidis (difference 17.48 months).ConclusionsThis extrapolation of survival data from ATTR-ACT further supports the efficacy of tafamidis in patients with ATTR-CM. Owing to the limitations of this analysis, these survival estimates should be interpreted with caution; however, they are consistent with recently presented findings from a combined analysis of data from ATTR-ACT and interim data from an ongoing long-term extension study (median follow-up 36 months; ClinicalTrials.gov number NCT02791230).Trial RegistrationClinicalTrials.gov: NCT01994889.Electronic supplementary materialThe online version of this article (10.1007/s40119-020-00179-2) contains supplementary material, which is available to authorized users.

Multistate Markov modelling of long-term outcomes in immunotherapy for metastatic renal cell carcinoma (mRCC) using machine-learned dynamic Bayesian networks.

e14049 Background: Patient-level heterogeneity in response to treatment remains a major challenge in cancer immunotherapy. Long-term individual-level modelling of survival, tumour response and safety outcomes jointly can help improve efficacy and durable clinical benefits. Machine-learned Bayesian networks provide a solution to the “black box” problem of other machine-learning approaches. Objectives: To develop a dynamic Bayesian network model for multivariate risk prediction, survival modelling and long-term simulations of immunotherapy patients using a simulated trial dataset. Methods: A simulated randomized clinical trial dataset for second line immunotherapy of patients with renal cell carcinoma was used for analysis. We machine-learned a dynamic Bayesian network from censored data with incorporation of prior clinical knowledge. Classification performance, probability calibration, goodness-of-fit metrics and prognostic variables were calculated following TRIPOD guidelines. Results: The machine-learned graphical model encoded expected relationships between variables with minimal prior information. Visual and numerical goodness-of-fit checks for survival extrapolations showed that the model fitted data well and was appropriate. Probability calibration was < 10% from ideal. Classification performance for overall survival was high ( c-statistic ~0.85) soon after treatment initiation and gradually plateaued over time. Prognostic variables were calculated by treatment arm for overall survival and severe adverse events. Conclusions: Dynamic Bayesian network model was useful for transparently representing joint relationships between all variables in the trial dataset, for multivariate risk prediction and long-term extrapolations that can be used in economic evaluations for health technology assessments in oncology.

Evaluation of survival extrapolation in immuno-oncology using multiple pre-planned data cuts: learnings to aid in model selection

BackgroundDue to limited duration of follow up in clinical trials of cancer treatments, estimates of lifetime survival benefits are typically derived using statistical extrapolation methods. To justify the method used, a range of approaches have been proposed including statistical goodness-of-fit tests and comparing estimates against a previous data cut (i.e. interim data collected). In this study, we extend these approaches by presenting a range of extrapolations fitted to four pre-planned data cuts from the JAVELIN Merkel 200 (JM200) trial. By comparing different estimates of survival and goodness-of-fit as JM200 data mature, we undertook an iterative process of fitting and re-fitting survival models to retrospectively identify early indications of likely long-term survival.MethodsStandard and spline-based parametric models were fitted to overall survival data from each JM200 data cut. Goodness-of-fit was determined using an assessment of the estimated hazard function, information theory-based methods and objective comparisons of estimation accuracy. Best-fitting extrapolations were compared to establish which one provided the most accurate estimation, and how statistical goodness-of-fit differed.ResultsSpline-based models provided the closest fit to the final JM200 data cut, though all extrapolation methods based on the earliest data cut underestimated the ‘true’ long-term survival (difference in restricted mean survival time [RMST] at 36 months: − 1.1 to − 0.5 months). Goodness-of-fit scores illustrated that an increasingly flexible model was favored as data matured. Given an early data cut, a more flexible model better aligned with clinical expectations could be reasonably justified using a range of metrics, including RMST and goodness-of-fit scores (which were typically within a 2-point range of the statistically ‘best-fitting’ model).ConclusionsSurvival estimates from the spline-based models are more aligned with clinical expectation and provided a better fit to the JM200 data, despite not exhibiting the definitively ‘best’ statistical goodness-of-fit. Longer-term data are required to further validate extrapolations, though this study illustrates the importance of clinical plausibility when selecting the most appropriate model. In addition, hazard-based plots and goodness-of-fit tests from multiple data cuts present useful approaches to identify when a more flexible model may be advantageous.Trial registrationJAVELIN Merkel 200 was registered with ClinicalTrials.gov as NCT02155647 on June 4, 2014.