Modelling the Survival Outcomes of Immuno-Oncology Drugs in Economic Evaluations: A Systematic Approach to Data Analysis and Extrapolation

Comparing Survival Extrapolation within All-Cause and Relative Survival Frameworks by Standard Parametric Models and Flexible Parametric Spline Models Using the Swedish Cancer Registry

BACKGROUND

In health technology assessment, restricted mean survival time and life expectancy are commonly evaluated. Parametric models are typically used for extrapolation. Spline models using a relative survival framework have been shown to estimate life expectancy of cancer patients more reliably; however, more research is needed to assess spline models using an all-cause survival framework and standard parametric models using a relative survival framework.

AIM

To assess survival extrapolation using standard parametric models and spline models within relative survival and all-cause survival frameworks.

METHODS

From the Swedish Cancer Registry, we identified patients diagnosed with 5 types of cancer (colon, breast, melanoma, prostate, and chronic myeloid leukemia) between 1981 and 1990 with follow-up until 2020. Patients were categorized into 15 cancer cohorts by cancer and age group (18-59, 60-69, and 70-99 y). We right-censored the follow-up at 2, 3, 5, and 10 y and fitted the parametric models within an all-cause and a relative survival framework to extrapolate to 10 y and lifetime in comparison with the observed Kaplan-Meier survival estimates. All cohorts were modeled with 6 standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, and generalized gamma) and 3 spline models (on hazard, odds, and normal scales).

RESULTS

For predicting 10-y survival, spline models generally performed better than standard parametric models. However, using an all-cause or a relative survival framework did not show any distinct difference. For lifetime survival, extrapolating from a relative survival framework agreed better with the observed survival, particularly using spline models.

CONCLUSIONS

For extrapolation to 10 y, we recommend spline models. For extrapolation to lifetime, we suggest extrapolating in a relative survival framework, especially using spline models.

HIGHLIGHTS

For survival extrapolation to 10 y, spline models generally performed better than standard parametric models did. However, using an all-cause or a relative survival framework showed no distinct difference under the same parametric model.Survival extrapolation to lifetime within a relative survival framework agreed well with the observed data, especially using spline models.Extrapolating parametric models within an all-cause survival framework may overestimate survival proportions at lifetime; models for the relative survival approach may underestimate instead.

CAR T-cell Therapy for Diffuse Large B-cell Lymphoma in Canada: A Cost-Utility Analysis

BACKGROUND

Chimeric antigen receptor (CAR) T-cell therapy is a novel cell therapy for treating non-Hodgkin lymphoma. The development of CAR T-cell therapy has transformed oncology treatment by offering a potential cure. However, due to the high cost of these therapies, and the large number of eligible patients, decision makers are faced with difficult funding decisions. Our objective was to assess the cost-effectiveness of tisagenlecleucel for adults with relapsed/refractory diffuse large B-cell lymphoma in Canada using updated survival data from the recent JULIET trial.

METHODS

We developed an individual-simulated discrete event simulation model to assess the costs and quality-adjusted life-years (QALY) of tisagenlecleucel compared with salvage chemotherapy. Survival estimates were obtained from a published clinical trial and retrospective analysis. If patients remained progression free for 5 y, they were assumed to be in long-term remission. Costing and utility data were obtained from reports and published sources. A Canadian health care payer perspective was used, and outcomes were modeled over a lifetime horizon. Costs and outcomes were discounted at 1.5% annually, with costs reported in 2021 Canadian dollars. A probabilistic analysis was used, and model parameters were varied in 1-way sensitivity analyses and scenario analyses.

RESULTS

After we incorporated the latest clinical evidence, tisagenlecleucel led to an additional cost of $503,417 and additional effectiveness of 2.48 QALYs, with an incremental cost-effectiveness ratio of $202,991 compared with salvage chemotherapy. At a willingness-to-pay threshold of $100,000/QALY, tisagenlecleucel had a 0% likelihood of being cost-effective.

CONCLUSIONS

At the current drug price, tisagenlecleucel was not found to be a cost-effective option. These results heavily depend on assumptions regarding long-term survival and the price of CAR T. Real-world evidence is needed to reduce uncertainty.

HIGHLIGHTS

For patients with diffuse large B-cell lymphoma who failed 2 or more lines of systemic therapy, CAR T was not found to be a cost-effective treatment option at a willingness-to-pay threshold of $100,000.These results heavily depend on the expected long-term survival. The uncertainty in the model may be improved using real-world evidence reported in the future.

Pembrolizumab alone or in combination with chemotherapy versus chemotherapy for advanced gastric cancer: A cost-effectiveness analysis

PURPOSE

The KEYNOTE-062 trial demonstrated the efficacy and safety of pembrolizumab for advanced gastric cancer (GC). The current study evaluated the cost-effectiveness of pembrolizumab alone or in combination with chemotherapy versus chemotherapy for advanced GC from the perspective of the United States and China. And the results will provide evidence and data support for more drug selection-related decisions and research in the future.

METHODS

A partitioned survival approach with three states was created for treatment of advanced GC. The survival data were derived from KEYNOTE-062 trial and the individual patient data were generated by a specific algorithm. We fitted 21 survival functions to each treatment arm and selected the most suitable distribution type for each one. Direct costs and utility values were collected from the published, available database. Cost, quality-adjusted life-years (QALYs), and incremental cost-utility ratios (ICURs) were considered as the primary measure outcomes. One-way and probabilistic sensitivity analyses were performed to assess the reliability of the analyses.

RESULTS

In the base-case analysis of combined positive score (CPS) ≥1 patients, the ICUR of pembrolizumab plus chemotherapy versus chemotherapy in American and Chinese setting is $345,209/QALY and $186,802.6/QALY, respectively. And the ICUR of pembrolizumab versus chemotherapy is $473,650/QALY and $377,753/QALY in the context of the US and China, respectively. For CPS≥10 patients, the ICUR of pembrolizumab plus chemotherapy versus chemotherapy in American and Chinese setting is $483,742/QALY and $262,965/QALY, respectively. And that of pembrolizumab versus chemotherapy is $96,550/QALY and $67,896/QALY in the context of the US and China.

CONCLUSION

Compared with chemotherapy, either pembrolizumab plus chemotherapy or pembrolizumab monotherapy is not regarded as a cost-effective strategy for patients with CPS≥1, advanced gastric cancer in the current American and Chinese setting. But pembrolizumab monotherapy for CPS≥10 patients would become a cost-effective option in the American setting.

Extrapolating empirical long-term survival data: the impact of updated follow-up data and parametric extrapolation methods on survival estimates in multiple myeloma

BACKGROUND

In economic evaluations, survival is often extrapolated to smooth out the Kaplan-Meier estimate and because the available data (e.g., from randomized controlled trials) are often right censored. Validation of the accuracy of extrapolated results can depend on the length of follow-up and the assumptions made about the survival hazard. Here, we analyze the accuracy of different extrapolation techniques while varying the data cut-off to estimate long-term survival in newly diagnosed multiple myeloma (MM) patients.

METHODS

Empirical data were available from a randomized controlled trial and a registry for MM patients treated with melphalan + prednisone, thalidomide, and bortezomib- based regimens. Standard parametric and spline models were fitted while artificially reducing follow-up by introducing database locks. The maximum follow-up for these locks varied from 3 to 13 years. Extrapolated (conditional) restricted mean survival time (RMST) was compared to the Kaplan-Meier RMST and models were selected according to statistical tests, and visual fit.

RESULTS

For all treatments, the RMST error decreased when follow-up and the absolute number of events increased, and censoring decreased. The decline in RMST error was highest when maximum follow-up exceeded six years. However, even when censoring is low there can still be considerable deviations in the extrapolated RMST conditional on survival until extrapolation when compared to the KM-estimate.

CONCLUSIONS

We demonstrate that both standard parametric and spline models could be worthy candidates when extrapolating survival for the populations examined. Nevertheless, researchers and decision makers should be wary of uncertainty in results even when censoring has decreased, and the number of events has increased.

Impact of Extrapolation Model Choices on the Structural Uncertainty in Economic Evaluations for Cancer Immunotherapy: A Case Study of Checkmate 067

OBJECTIVES

The aim of this study was to compare the performance of different extrapolation modeling techniques and analyze their impact on structural uncertainties in the economic evaluations of cancer immunotherapy.

METHODS

The individual patient data was reconstructed through published Checkmate 067 Kaplan Meier curves. Standard parametric models and six flexible techniques were tested, including fractional polynomial, restricted cubic splines, Royston-Parmar models, generalized additive models, parametric mixture models, and mixture cure models. Mean square errors (MSE) and bias from raw survival plots were used to test the model fitness and extrapolation performance. Variability of estimated incremental cost-effectiveness ratios (ICERs) from different models was used to inform the structural uncertainty in economic evaluations. All indicators were analyzed and compared under cut-offs of 3 years and 6.5 years, respectively, to further discuss model impact under different data maturity. R Codes for reproducing this study can be found on GitHub.

RESULTS

The flexible techniques in general performed better than standard parametric models with smaller MSE irrespective of the data maturity. Survival outcomes projected by long-term extrapolation using immature data differed from those with mature data. Although a best-performing model was not found because several models had very similar MSE in this case, the variability of modeled ICERs significantly increased when prolonging simulation cycles.

CONCLUSIONS

Flexible techniques show better performance in the case of Checkmate 067, regardless of data maturity. Model choices affect ICERs of cancer immunotherapy, especially when dealing with immature survival data. When researchers lack evidence to identify the 'right' model, we recommend identifying and revealing the model impacts on structural uncertainty.

Blended Survival Curves: A New Approach to Extrapolation for Time-to-Event Outcomes from Clinical Trials in Health Technology Assessment

BACKGROUND

Survival extrapolation is essential in cost-effectiveness analysis to quantify the lifetime survival benefit associated with a new intervention, due to the restricted duration of randomized controlled trials (RCTs). Current approaches of extrapolation often assume that the treatment effect observed in the trial can continue indefinitely, which is unrealistic and may have a huge impact on decisions for resource allocation.

OBJECTIVE

We introduce a novel methodology as a possible solution to alleviate the problem of survival extrapolation with heavily censored data from clinical trials.

METHOD

The main idea is to mix a flexible model (e.g., Cox semiparametric) to fit as well as possible the observed data and a parametric model encoding assumptions on the expected behavior of underlying long-term survival. The two are "blended" into a single survival curve that is identical with the Cox model over the range of observed times and gradually approaching the parametric model over the extrapolation period based on a weight function. The weight function regulates the way two survival curves are blended, determining how the internal and external sources contribute to the estimated survival over time.

RESULTS

A 4-y follow-up RCT of rituximab in combination with fludarabine and cyclophosphamide versus fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia is used to illustrate the method.

CONCLUSION

Long-term extrapolation from immature trial data may lead to significantly different estimates with various modelling assumptions. The blending approach provides sufficient flexibility, allowing a wide range of plausible scenarios to be considered as well as the inclusion of external information, based, for example, on hard data or expert opinion. Both internal and external validity can be carefully examined.

HIGHLIGHTS

Interim analyses of trials with limited follow-up are often subject to high degrees of administrative censoring, which may result in implausible long-term extrapolations using standard approaches.In this article, we present an innovative methodology based on "blending" survival curves to relax the traditional proportional hazard assumption and simultaneously incorporate external information to guide the extrapolation.The blended method provides a simple and powerful framework to allow a careful consideration of a wide range of plausible scenarios, accounting for model fit to the short-term data as well as the plausibility of long-term extrapolations.

Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity

Aim: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH) WMC NMA to inform long-term survival of therapies. Materials & methods: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumab and atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. Results: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15) and 0.03 (0.00; 0.09) for docetaxel, nivolumab, pembrolizumab and atezolizumab, respectively, with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37) and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20) and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96) and 1.36 (-0.05; 3.64). Conclusion: BH-WMC-NMA impacts incremental mean LYs and cost-effectiveness ratios, potentially affecting reimbursement decisions.

Development and validation of a decision model for the evaluation of novel lung cancer treatments in the Netherlands

Recent discoveries in molecular diagnostics and drug treatments have improved the treatment of patients with advanced (inoperable) non-squamous non-small cell lung cancer (NSCLC) from solely platinum-based chemotherapy to more personalized treatment, including targeted therapies and immunotherapies. However, these improvements come at considerable costs, highlighting the need to assess their cost-effectiveness in order to optimize lung cancer care. Traditionally, cost-effectiveness models for the evaluation of new lung cancer treatments were based on the findings of the randomized control trials (RCTs). However, the strict RCT inclusion criteria make RCT patients not representative of patients in the real-world. Patients in RCTs have a better prognosis than patients in a real-world setting. Therefore, in this study, we developed and validated a diagnosis-treatment decision model for patients with advanced (inoperable) non-squamous NSCLC based on real-world data in the Netherlands. The model is a patient-level microsimulation model implemented as discrete event simulation with five health events. Patients are simulated from diagnosis to death, including at most three treatment lines. The base-model (non-personalized strategy) was populated using real-world data of patients treated with platinum-based chemotherapy between 2008 and 2014 in one of six Dutch teaching hospitals. To simulate personalized care, molecular tumor characteristics were incorporated in the model based on the literature. The impact of novel targeted treatments and immunotherapies was included based on published RCTs. To validate the model, we compared survival under a personalized treatment strategy with observed real-world survival. This model can be used for health-care evaluation of personalized treatment for patients with advanced (inoperable) NSCLC in the Netherlands.

Comparison of statistical methods for extrapolating survival in previously untreated diffuse large B-cell lymphoma: results based on the POLARIX study

OBJECTIVE

The ongoing Phase III randomized POLARIX study (GO39942; NCT03274492) demonstrated significantly improved progression-free survival (PFS) with polatuzumab vedotin plus rituximab, cyclophosphamide, doxorubicin and prednisone (Pola-R-CHP) versus rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) in patients with previously untreated diffuse large B-cell lymphoma (DLBCL). We compared statistical methodologies to extrapolate long-term PFS data from POLARIX.

MATERIALS AND METHODS

This analysis explored four different approaches to extrapolate the POLARIX data: standard parametric survival, mixture-cure, landmark, and spline models. The resulting extrapolation curves were validated via comparison with the corresponding Kaplan-Meier (KM) curves from POLARIX and the POLARIX-like population of the Phase III GOYA study (NCT01287741; R-CHOP arm).

RESULTS

The R-CHOP PFS KM curve from the GOYA validation set was well aligned with the POLARIX KM curve. As we anticipated that PFS in POLARIX would evolve similarly to that of GOYA, the data from GOYA were used to externally validate the extrapolated modelling results. While all four statistical methods were able to fit the data to the POLARIX KM curve, the mixture-cure model was the most accurate in predicting long-term PFS in the GOYA external validation set. In the mixture-cure model, generalized gamma distribution estimated 64% (95% confidence intervals [CI]: 56-71%) of patients to have long-term remission in the R-CHOP arm of POLARIX and GOYA, and 75% (95% CI: 70-79%) in the Pola-R-CHP arm of POLARIX. A limitation of this study was the comparison of the statistical models only in the PFS KM curves, since it was not possible to determine which statistical method was more appropriate to extrapolate the overall survival KM curves.

CONCLUSIONS

Within this analysis, the mixture-cure model provided the best prediction of long-term outcomes from the primary PFS analysis of the POLARIX study.

A Flexible Ensemble Learning Method for Survival Extrapolation

OBJECTIVES

Survival extrapolation is an important statistical concept for estimating long-term survival from short-term clinical trial data. It is widely used in health technology assessment (HTA). Survival extrapolation is often performed by fitting one or two parametric models selected based on experience or selecting a model based on some goodness of fit statistics from a predefined collection of models. The main challenge in survival extrapolation is that the result is sensitive to model misspecification. In this study, we aim to propose a new approach that has a robust performance for survival extrapolation.

METHODS

We propose a new method called Ensemble Learning for Survival Extrapolation (ELSE). Instead of selecting one best model from a predefined collection, ELSE builds an ensemble model based on a collection of models from the model library. Under this framework, we construct a point estimate of the long-term survival with a weighted average of the estimates of all candidate models and derive confidence intervals using nonparametric bootstrap.

RESULTS

With our extensive numerical simulation studies, the proposed ELSE method shows better performance than the traditionally used model selection procedure based on Akaike Information Criterion (AIC). With a real data application to the Therapeutically Applicable Research to Generate Effective Treatment Wilms Tumor project (TARGET-WT) data, the ELSE method produces better survival extrapolation results in point estimate accuracy and confidence interval coverage.

CONCLUSIONS

We developed an ensemble learning method for survival extrapolation (ELSE) which is robust for the underline data model and has good real data performance.

Cost-effectiveness analysis of sintilimab vs. placebo in combination with chemotherapy as first-line therapy for local advanced or metastatic oesophageal squamous cell carcinoma

Objective

Results of Orient 15 indicated the health benefits to patients with local advanced or metastatic oesophageal squamous cell carcinoma (OSCC). This study aimed to evaluate the cost-effectiveness of sintilimab plus chemotherapy in treating OSCC from the perspective of Chinese healthcare system.

Methods

A partitioned survival model was constructed to evaluate the cost-effectiveness of sintilimab plus chemotherapy vs. chemotherapy in treating OSCC. Baseline characteristics of patients and key clinical data were extracted from Orient 15. Costs and utilities were collected from published studies and open-access databases. Costs, quality-adjusted life-years (QALYs), life-years gained, and incremental cost-effectiveness ratios (ICER) were chosen as economic outcome indicators. We also performed sensitivity analyses and subgroup analyses to verify the stability of results.

Results

Combination therapy provided additional 0.84 QALYs and 1.46 life-years with an incremental cost of $25,565.48 than chemotherapy, which had an ICER of $30,409.44 per QALY. The probabilistic sensitivity analysis indicated that combination therapy had a 98.8% probability of cost-effectiveness at the willingness-to-pay threshold (WTP) of $38,184 per QALY. Deterministic sensitivity analysis showed that model outcomes were sensitive to the utilities of progression-free survival and progression disease. The subgroup analysis revealed that combination therapy was cost-effective in patients with high expression of PD-L1 and several specific subgroups.

Conclusion

In this economic evaluation, sintilimab plus chemotherapy was likely to be cost-effective compared with chemotherapy in the first-line therapy of advanced OSCC from the perspective of Chinese healthcare system. Our findings may provide evidence for clinicians to make optimal decisions in clinical practice and for decision-makers to evaluate the cost-effectiveness of sintilimab.

Cost-Effectiveness Analysis of Atezolizumab Versus Durvalumab as First-Line Treatment of Extensive-Stage Small-Cell Lung Cancer in the USA

BACKGROUND AND OBJECTIVES

Durvalumab and atezolizumab are approved as first-line therapy in extensive-stage small-cell lung cancer. Although cost-effectiveness analyses compared these immunotherapy drugs with standard chemotherapy-alone regimens, no head-to-head cost-effectiveness comparisons for these treatments exist. The aim of the present analysis is to determine the cost-effectiveness of durvalumab and atezolizumab as first-line therapy for extensive-stage small-cell lung cancer from the US payers' perspective.

METHODS

This study is based on two placebo-controlled, phase 3 clinical trials: CASPIAN and IMpower133. A Markov model was developed to simulate the three health states: progression-free survival, progressed disease, and death in patients with extensive-stage small-cell lung cancer. Transition probabilities were estimated from the clinical trial survival curves and extended with life-time modelling. Health utilities and direct costs of adverse event treatment were included. Main outcome was the incremental cost-effectiveness ratio (ICER) using quality-adjusted life-years saved (QALYS). Sensitivity analysis was performed to assess the impact of variables on the ICER.

RESULTS

Durvalumab group has a cost of $187,503 with an effectiveness of 1.08 while atezolizumab has a cost of $160,219 and an effectiveness of 0.932. Durvalumab is not cost-effective compared to atezolizumab with an ICER of $165,182 QALYS, which is over the willingness-to-pay threshold of $150,000. The model was most sensitive to durvalumab cost and the cost of treating durvalumab adverse effects.

CONCLUSIONS

With the ICER of durvalumab treatment group being very close to $150,000, setting a higher willingness-to-pay threshold or decreasing the drug cost through contract pricing can increase the cost-effectiveness of durvalumab compared to atezolizumab.

The Cost Effectiveness of Donafenib Compared With Sorafenib for the First-Line Treatment of Unresectable or Metastatic Hepatocellular Carcinoma in China

Background

Recent clinical trials have demonstrated that donafenib has superior efficacy and safety compared with sorafenib in Chinese patients with unresectable or metastatic hepatocellular carcinoma (HCC). The objective of this study was to assess the cost effectiveness of donafenib compared with sorafenib for the treatment of patients with unresectable or metastatic HCC in China.

Methods

A three-state partitioned survival model was developed to perform a cost-effectiveness analysis comparing donafenib and sorafenib from a Chinese healthcare payer's perspective. The model adopted a lifetime horizon and a 4-week cycle length. Survival data were derived from the ZGDH3 study and fitted with standard parametric functions for extrapolation beyond the trial period. Cost data were obtained from the mean price of publicly listed online bids in 2021 and medical service prices across provinces in China. Utility data were obtained from previous literature. The cost and health outcomes were discounted at an annual rate of 5%. Deterministic and probabilistic sensitivity analyses (PSAs) were carried out to verify the robustness of the model.

Results

Compared with sorafenib, donafenib incurred a higher cost (US$22,330.23 vs. US$14,775.92) but yielded more quality-adjusted life years (1.045 vs. 0.861 QALYs). The incremental cost-effectiveness ratio (ICER) for donafenib was US$41,081.52 per QALY gained (ICER = US$13,439.10/QALY). The PSA results indicated that at a willingness-to-pay threshold of 3 times the GDP in China, the probability of donafenib being cost effective was 16.9%. The ICER (US$13,439.10/QALY) decreased when the branded price of sorafenib was used in the model.

Conclusions

Donafenib is unlikely to be cost effective compared with sorafenib for the first-line treatment of unresectable or metastatic HCC in China. Reducing the price of donafenib can increase the possibility of it being cost effective in the future.

Methods for Extrapolating Survival Analyses for the Economic Evaluation of Advanced Therapy Medicinal Products

There are two significant challenges for analysts conducting economic evaluations of advanced therapy medicinal products (ATMPs): (i) estimating long-term treatment effects in the absence of mature clinical data, and (ii) capturing potentially complex hazard functions. This review identifies and critiques a variety of methods that can be used to overcome these challenges. The narrative review is informed by a rapid literature review of methods used for the extrapolation of survival analyses in the economic evaluation of ATMPs. There are several methods that are more suitable than traditional parametric survival modelling approaches for capturing complex hazard functions, including, cure-mixture models and restricted cubic spline models. In the absence of mature clinical data, analysts may augment clinical trial data with data from other sources to aid extrapolation, however, the relative merits of employing methods for including data from different sources is not well understood. Given the high and potentially irrecoverable costs of making incorrect decisions concerning the reimbursement or commissioning of ATMPs, it is important that economic evaluations are correctly specified, and that both parameter and structural uncertainty associated with survival extrapolations are considered. Value of information analyses allow for this uncertainty to be expressed explicitly, and in monetary terms.

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration.

Heterogeneity in Survival with Immune Checkpoint Inhibitors and Its Implications for Survival Extrapolations: A Case Study in Advanced Melanoma

Background

Survival heterogeneity and limited trial follow-up present challenges for estimating lifetime benefits of oncology therapies. This study used CheckMate 067 (NCT01844505) extended follow-up data to assess the predictive accuracy of standard parametric and flexible models in estimating the long-term overall survival benefit of nivolumab plus ipilimumab (an immune checkpoint inhibitor combination) in advanced melanoma.

Methods

Six sets of survival models (standard parametric, piecewise, cubic spline, mixture cure, parametric mixture, and landmark response models) were independently fitted to overall survival data for treatments in CheckMate 067 (nivolumab plus ipilimumab, nivolumab, and ipilimumab) using successive data cuts (28, 40, 52, and 60 mo). Standard parametric models allow survival extrapolation in the absence of a complex hazard. Piecewise and cubic spline models allow additional flexibility in fitting the hazard function. Mixture cure, parametric mixture, and landmark response models provide flexibility by explicitly incorporating survival heterogeneity. Sixty-month follow-up data, external ipilimumab data, and clinical expert opinion were used to evaluate model estimation accuracy. Lifetime survival projections were compared using a 5% discount rate.

Results

Standard parametric, piecewise, and cubic spline models underestimated overall survival at 60 mo for the 28-mo data cut. Compared with other models, mixture cure, parametric mixture, and landmark response models provided more accurate long-term overall survival estimates versus external data, higher mean survival benefit over 20 y for the 28-mo data cut, and more consistent 20-y mean overall survival estimates across data cuts.

Conclusion

This case study demonstrates that survival models explicitly incorporating survival heterogeneity showed greater accuracy for early data cuts than standard parametric models did, consistent with similar immune checkpoint inhibitor survival validation studies in advanced melanoma. Research is required to assess generalizability to other tumors and disease stages.

Highlights

Assessment of Treatment Effects and Long-term Benefits in Immune Checkpoint Inhibitor Trials Using the Flexible Parametric Cure Model: A Systematic Review

IMPORTANCE

Compared with standard cytotoxic therapies, randomized immune checkpoint inhibitor (ICI) phase 3 trials reveal delayed benefits in terms of patient survival and/or long-term response. Such outcomes generally violate the assumption of proportional hazards, and the classical Cox proportional hazards regression model is therefore unsuitable for these types of analyses.

OBJECTIVE

To evaluate the ability of the flexible parametric cure model (FPCM) to estimate treatment effects and long-term responder fractions (LRFs) independently of prespecified time points.

EVIDENCE REVIEW

This systematic review used reconstructed individual patient data from ICI advanced or metastatic melanoma and lung cancer phase 3 trials extracted from the literature. Trials published between January 1, 2010, and October 1, 2019, with long-term follow-up periods (maximum follow-up, ≥36 months in first line and ≥30 months otherwise) were selected to identify LRFs. Individual patient data for progression-free survival were reconstructed from the published randomized ICI phase 3 trial results. The FPCM was applied to estimate treatment effects on the overall population and on the following components of the population: LRF and progression-free survival in non-long-term responders. Results obtained were compared with treatment effects estimated using the Cox proportional hazards regression model.

FINDINGS

In this systematic review, among the 23 comparisons studied using the FPCM, a statistically significant association between the time-to-event component and experimental treatment was observed in the main analyses and confirmed in the sensitivity analyses of 18 comparisons. Results were discordant for 4 comparisons that were not significant by the Cox proportional hazards regression model. The LRFs varied from 1.5% to 12.7% for the control arms and from 4.6% to 38.8% for the experimental arms. Differences in LRFs varied from 2% to 29% and were significantly increased in the experimental compared with the control arms, except for 4 comparisons.

CONCLUSIONS AND RELEVANCE

This systematic review of reconstructed individual patient data found that the FPCM was a complementary approach that provided a comprehensive and pertinent evaluation of benefit and risk by assessing whether ICI treatment was associated with an increased probability of patients being long-term responders or with an improved progression-free survival in patients who were not long-term responders.

Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study

BACKGROUND

Estimates of future survival can be a key evidence source when deciding if a medical treatment should be funded. Current practice is to use standard parametric models for generating extrapolations. Several emerging, more flexible, survival models are available which can provide improved within-sample fit. This study aimed to assess if these emerging practice models also provided improved extrapolations.

METHODS

Both a simulation study and a case-study were used to assess the goodness of fit of five classes of survival model. These were: current practice models, Royston Parmar models (RPMs), Fractional polynomials (FPs), Generalised additive models (GAMs), and Dynamic survival models (DSMs). The simulation study used a mixture-Weibull model as the data-generating mechanism with varying lengths of follow-up and sample sizes. The case-study was long-term follow-up of a prostate cancer trial. For both studies, models were fit to an early data-cut of the data, and extrapolations compared to the known long-term follow-up.

RESULTS

The emerging practice models provided better within-sample fit than current practice models. For data-rich simulation scenarios (large sample sizes or long follow-up), the GAMs and DSMs provided improved extrapolations compared with current practice. Extrapolations from FPs were always very poor whilst those from RPMs were similar to current practice. With short follow-up all the models struggled to provide useful extrapolations. In the case-study all the models provided very similar estimates, but extrapolations were all poor as no model was able to capture a turning-point during the extrapolated period.

CONCLUSIONS

Good within-sample fit does not guarantee good extrapolation performance. Both GAMs and DSMs may be considered as candidate extrapolation models in addition to current practice. Further research into when these flexible models are most useful, and the role of external evidence to improve extrapolations is required.

The Extrapolation Performance of Survival Models for Data With a Cure Fraction: A Simulation Study

OBJECTIVES

Curative treatments can result in complex hazard functions. The use of standard survival models may result in poor extrapolations. Several models for data which may have a cure fraction are available, but comparisons of their extrapolation performance are lacking. A simulation study was performed to assess the performance of models with and without a cure fraction when fit to data with a cure fraction.

METHODS

Data were simulated from a Weibull cure model, with 9 scenarios corresponding to different lengths of follow-up and sample sizes. Cure and noncure versions of standard parametric, Royston-Parmar, and dynamic survival models were considered along with noncure fractional polynomial and generalized additive models. The mean-squared error and bias in estimates of the hazard function were estimated.

RESULTS

With the shortest follow-up, none of the cure models provided good extrapolations. Performance improved with increasing follow-up, except for the misspecified standard parametric cure model (lognormal). The performance of the flexible cure models was similar to that of the correctly specified cure model. Accurate estimates of the cured fraction were not necessary for accurate hazard estimates. Models without a cure fraction provided markedly worse extrapolations.

CONCLUSIONS

For curative treatments, failure to model the cured fraction can lead to very poor extrapolations. Cure models provide improved extrapolations, but with immature data there may be insufficient evidence to choose between cure and noncure models, emphasizing the importance of clinical knowledge for model choice. Dynamic cure fraction models were robust to model misspecification, but standard parametric cure models were not.

Finite Mixture Models, a Flexible Alternative to Standard Modeling Techniques for Extrapolated Mean Survival Times Needed for Cost-Effectiveness Analyses

OBJECTIVES

To compare finite mixture models with common survival models with respect to how well they fit heterogenous data used to estimate mean survival times required for cost-effectiveness analysis.

METHODS

Publicly available overall survival (OS) and progression-free survival (PFS) curves were digitized to produce nonproprietary data. Regression models based on the following distributions were fit to the data: Weibull, lognormal, log-logistic, generalized F, generalized gamma, Gompertz, mixture of 2 Weibulls, and mixture of 3 Weibulls. A second set of analyses was performed based on data in which patients who had not experienced an event by 30 months were censored. Model performance was compared based on the Akaike information criterion (AIC).

RESULTS

For PFS, the 3-Weibull mixture (AIC = 479.94) and 2-Weibull mixture (AIC = 488.24) models outperformed other models by more than 40 points and produced the most accurate estimates of mean survival times. For OS, the AIC values for all models were similar (all within 4 points). The means for the mixture 3-Weibulls mixture model (17.60 months) and the 2-Weibull mixture model (17.59 months) were the closest to the Kaplan-Meier mean estimate of (17.58 months). The results and conclusions from the censored analysis of PFS were similar to the uncensored PFS analysis. On the basis of extrapolated mean OS, all models produced estimates within 10% of the Kaplan-Meier mean survival time.

CONCLUSIONS

Finite mixture models offer a flexible modeling approach that has benefits over standard parametric models when analyzing heterogenous data for estimating survival times needed for cost-effectiveness analysis.

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 responses and prolonged survival in some patients. However, providing robust evidence of the long-term benefits of IOs at health technology assessment (HTA) submission presents several challenges for manufacturers. The aim of this article was to identify, analyze, categorize, and further explore the key challenges that regulators, HTA agencies, and payers commonly encounter when assessing the long-term benefits of IO therapies. Insights were obtained from an international, multi-stakeholder steering committee (SC) and expert panels comprising of payers, economists, and clinicians. The selected individuals were tasked with developing a summary of challenges specific to IOs in demonstrating their long-term benefits at HTA submission. The SC and expert panels agreed that standard methods used to assess the long-term benefit of anticancer drugs may have limitations for IO therapies. Three key areas of challenges were identified: (1) lack of a disease model that fully captures the mechanism of action and subsequent patient responses; (2) estimation of longer-term outcomes, including a lack of agreement on ideal methods of survival analyses and extrapolation of survival curves; and (3) data limitations at the time of HTA submission, for which surrogate survival end points and real-world evidence could prove useful. A summary of the key challenges facing manufacturers when submitting evidence at HTA submission was developed, along with further recommendations for manufacturers in what evidence to produce. Despite almost a decade of use, there remain significant challenges around how best to demonstrate the long-term benefit of checkpoint inhibitor-based IOs to HTA agencies, clinicians, and payers. Manufacturers can potentially meet or mitigate these challenges with a focus on strengthening survival analysis methodology. Approaches to doing this include identifying reliable biomarkers, intermediate and surrogate end points, and the use of real-world data to inform and validate long-term survival projections. Wider education across all stakeholders—manufacturers, payers, and clinicians—in considering the long-term survival benefit with IOs is also important.

Developing a Framework for the Health Technology Assessment of Histology-independent Precision Oncology Therapies

The arrival of precision oncology is challenging the evidence standards under which technologies are evaluated for regulatory approval as well as for health technology assessment (HTA) purposes. Several key concepts are discussed to highlight the source of the challenges in evaluating these products, particularly those impacting the HTA of histology-independent therapies. These include the basket trial design, high uncertainty in (potentially substantial) benefits for histology-independent therapies, and the inability to identify and quantify benefits of standard of care in daily practice when the biomarker is not currently used in practice. There is little precedent for a technology with the unique mixture of challenges for HTA of histology-independent therapies and they will be evaluated using standard HTA, as there currently is no evidence suggesting the standard HTA framework is not appropriate. A number of questions proposed to help guide HTA bodies when assessing the appropriateness of local processes to optimally evaluate histology-independent therapies. Pragmatic solutions are further proposed to decrease uncertainty in the benefits of histology independent therapies as well as fill gaps in comparative evidence. The proposed solutions ensure a consistent and streamlined approach to evaluation across histology-independent products, although with varying strengths and limitations. Alongside these solutions, sponsors should engage early with HTA bodies/payers and regulatory agencies through parallel/joint scientific advice to facilitate the integration of both regulatory and HTA perspectives into one clinical development programme, potentially reconciling evidence requirements.

Applying State-of-the-Art Survival Extrapolation Techniques to the Evaluation of CAR-T Therapies: Evidence from a Systematic Literature Review

INTRODUCTION

Traditional statistical techniques for extrapolating short-term survival data for anticancer therapies assume the same mortality rate for noncured and "cured" patients, which is appropriate for projecting survival of non-curative therapies but may lead to an underestimation of the treatment effectiveness for potentially curative therapies. Our objective was to ascertain research trends in survival extrapolation techniques used to project the survival benefits of chimeric antigen receptor T cell (CAR-T) therapies.

METHODS

A global systematic literature search produced a review of survival analyses of CAR-T therapies, published between January 1, 2015 and December 14, 2020, based on publications sourced from MEDLINE, scientific conferences, and health technology assessment agencies. Trends in survival extrapolation techniques used, and the rationale for selecting advanced techniques, are discussed.

RESULTS

Twenty publications were included, the majority of which (65%, N = 13) accounted for curative intent of CAR-T therapies through the use of advanced extrapolation techniques, i.e., mixture cure models [MCMs] (N = 10) or spline-based models (N = 3). The authors' rationale for using the MCM approach included (a) better statistical fits to the observed Kaplan-Meier curves (KMs) and (b) visual inspection of the KMs indicated that a proportion of patients experienced long-term remission and survival which is not inherently captured in standard parametric distributions.

DISCUSSION

Our findings suggest that an advanced extrapolation technique should be considered in base case survival analyses of CAR-T therapies when extrapolating short-term survival data to long-term horizons extending beyond the clinical trial duration.

CONCLUSION

Advanced extrapolation techniques allow researchers to account for the proportion of patients with an observed plateau in survival from clinical trial data; by only using standard-partitioned modeling, researchers may risk underestimating the survival benefits for the subset of patients with long-term remission. Sensitivity analysis with an alternative advanced extrapolation technique should be implemented and re-assessment using clinical trial extension data and/or real-world data should be conducted as longer-term data become available.

Mixture Cure Models in Oncology: A Tutorial and Practical Guidance

Novel cancer therapies are associated with survival patterns that differ from established therapies, which may include survival curves that plateau after a certain follow-up time point. A fraction of the patient population is then considered statistically cured and subject to the same mortality experience as the cancer-free general population. Mixture cure models have been developed to account for this characteristic. As compared to standard survival analysis, mixture cure models can often lead to profoundly different estimates of long-term survival, required for health economic evaluations. This tutorial is designed as a practical introduction to mixture cure models. Step-by-step instructions are provided for the entire implementation workflow, i.e., from gathering and combining data from different sources to fitting models using maximum likelihood estimation and model results interpretation. Two mixture cure models were developed to illustrate (1) an "uninformed" approach where the cure fraction is estimated from trial data and (2) an "informed" approach where the cure fraction is obtained from an external source (e.g., real-world data) used as an input to the model. These models were implemented in the statistical software R, with the freely available code on GitHub. The cure fraction can be estimated as an output from ("uninformed" approach) or used as an input to ("informed" approach) a mixture cure model. Mixture cure models suggest presumed estimates of long-term survival proportions, especially in instances where some fraction of patients is expected to be statistically cured. While this type of model may initially seem complex, it is straightforward to use and interpret. Mixture cure models have the potential to improve the accuracy of survival estimates for treatments associated with statistical cure, and the present tutorial outlines the interpretation and implementation of mixture cure models in R. This type of model will likely become more widely used in health economic analyses as novel cancer therapies enter the market.

Extrapolation of Survival Curves Using Standard Parametric Models and Flexible Parametric Spline Models: Comparisons in Large Registry Cohorts with Advanced Cancer

BACKGROUND

It is often important to extrapolate survival estimates beyond the limited follow-up times of clinical trials. Extrapolated survival estimates can be highly sensitive to model choice; thus, appropriate model selection is crucial. Flexible parametric spline models have been suggested as an alternative to standard parametric models; however, their ability to extrapolate is not well understood.

AIM

To determine how well standard parametric and flexible parametric spline models predict survival when fitted to registry cohorts with artificially right-censored follow-up times.

METHODS

Adults with advanced breast, colorectal, small cell lung, non-small cell lung, or pancreatic cancer with a potential follow-up time of 10 y were selected from the SEER 1973-2015 registry data set. Patients were classified into 15 cohorts by cancer and age group at diagnosis (18-59, 60-69, 70+ y). Follow-up times for each cohort were right censored at 20%, 35%, and 50% survival. Standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, generalized gamma) and spline models (proportional hazards, proportional odds, normal/probit) were fitted to the 10-y data set and the 3 right-censored data sets. Predicted 10-y restricted mean survival time and percentage surviving at 10 y were compared with the observed values.

RESULTS

Across all data sets, the spline odds and spline normal models most frequently gave accurate predictions of 10-y survival outcomes. Visually, spline models tended to demonstrate better fit to the observed hazard functions than standard parametric models, both in the censored and 10-y data.

CONCLUSIONS

In these cohorts, where there was little uncertainty in the observed data, the spline models performed well when extrapolating beyond the observed data. Spline models should be routinely included in the set of models that are fitted when extrapolating cancer survival data.

Effect of Treatment with the PD-1/PD-L1 Inhibitors on Key Health Outcomes of Cancer Patients

BACKGROUND

Recent studies have shown that treatment with the programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitor class could significantly improve survival outcomes in several oncology indications. However, there is some clinical uncertainty.

OBJECTIVE

This study aimed to obtain high-level estimates of the impact of treatment with PD-1/PD-L1 inhibitor class to oncology treatment on key health outcomes in real-world situations and to inform public health policy decisions about cancer care after reducing uncertainties around new immuno-oncology therapy options in South Korea.

METHODS

A model was developed to estimate the impact of PD-1/PD-L1 inhibitors on outcomes in situations wherein both anti-PD-1/PD-L1s and standard of care (SOC) were available versus SOC only. A partitioned survival model was utilized to estimate the impact of introducing anti-PD-1/PD-L1s on outcomes, including life-years gained, quality-adjusted life-years gained, progression-free survival-years obtained, and grade 3 or higher adverse events avoided for six indications over 5 years. An exponential distribution was fitted to the survival function of the SOC based on visual inspection. Outcomes associated with anti-PD-1/PD-L1s were estimated using a piecewise modeling approach with Kaplan-Meier analysis followed by best-fitting survival analysis. The incident number of patients and market share of anti-PD-1/PD-L1s during 2020-2024 were projected using published literature and Korean market survey data. Sensitivity analyses were performed to test the uncertainty of input parameters.

RESULTS

During the next 5-year period (2020-2024), introducing the anti-PD-1/PD-L1 class led to a gain of 22,001 life-years (+ 31%), 19,073 quality-adjusted life-years (+ 38%), and 22,893 progression-free survival-years (+ 82%); it also avoided 3610 adverse events (- 11%) compared with SOC alone. Most adverse events associated with anti-PD-1/PD-L1s were attributed to combination therapy with cytotoxic chemotherapy (91%). In a scenario wherein the time to reimbursement of the anti-PD-1/PD-L1s was accelerated by 1 year, the life-years gained increased by 14% compared with the base-case scenario.

CONCLUSIONS

Anti-PD-1/PD-L1 therapy is expected to provide marked survival benefits for patients with cancer. This study demonstrated the potentially beneficial health impacts of utilizing the anti-PD-1/PD-L1 class at the population level. The findings could inform health policy decision makers about cancer care and ultimately enhance population health through rapid access to innovative cancer drugs.

Recalibrating Health Technology Assessment Methods for Cell and Gene Therapies

Recently licensed cell and gene therapies have promising but highly uncertain clinical benefits. They are entering the market at very high prices, with the latest entrants costing hundreds of thousands of dollars. The significant long-term uncertainty posed by these therapies has already complicated the use of conventional economic evaluation approaches such as cost-effectiveness and cost-utility analyses, which are widely used for assessing the value of new health interventions. Cell and gene therapies also risk jeopardising healthcare systems' financial sustainability. As a result, there is a need to recalibrate the current health technology assessment methods used to measure and compensate their value. In this paper, we outline a set of technical adaptations and methodological refinements to address key challenges in the appraisal of cell and gene therapies' value, including the assessment of efficiency and affordability. We also discuss the potential role of alternative financing mechanisms. Ultimately, uncertainties associated with cell and gene therapies can only be meaningfully addressed by improving the evidence base supporting their approval and adoption in healthcare systems.

A cost-utility analysis of atezolizumab in the second-line treatment of patients with metastatic bladder cancer

Background

Despite initial promising results, the IMvigor211 clinical trial failed to demonstrate an overall survival (os) benefit for atezolizumab compared with chemotherapy as second-line treatment for metastatic bladder cancer (mbc). However, given lessened adverse events (aes) and preserved quality of life (qol) with atezolizumab, there might still be investment value. To evaluate that potential value, we conducted a cost-utility analysis (cua) of atezolizumab compared with chemotherapy from the perspective of the Canadian health care payer.

Methods

A partitioned survival model was used to evaluate atezolizumab compared with chemotherapy over a lifetime horizon (5 years). The base-case analysis was conducted for the intention-to-treat (itt) population, with additional scenario analyses for subgroups by IMvigor-defined PD-L1 status. Health outcomes were evaluated through life-year gains and quality-adjusted life-years (qalys). Cost estimates in 2018 Canadian dollars for systemic treatment, aes, and end-of-life care were incorporated. The incremental cost-effectiveness ratio (icer) was used to compare treatment strategies. Parameter and model uncertainty were assessed through sensitivity and scenario analyses. Per Canadian guidelines, cost and effectiveness were discounted at 1.5%.

Results

For the itt population, the expected qalys for atezolizumab and chemotherapy were 0.75 and 0.56, with expected costs of $90,290 and $8,466 respectively. The resultant icer for atezolizumab compared with chemotherapy was $430,652 per qaly. Scenario analysis of patients with PD-L1 expression levels of 5% or greater led to a lower icer ($334,387 per qaly). Scenario analysis of observed compared with expected benefits demonstrated a higher icer, with a shorter time horizon ($928,950 per qaly).

Conclusions

Despite lessened aes and preserved qol, atezolizumab is not considered cost-effective for the second-line treatment of mbc.

Economic Evaluation of Single versus Combination Immuno-Oncology Therapies: Application of a Novel Modelling Approach in Metastatic Melanoma

Background

Existing economic model frameworks may not adequately capture the atypical treatment response patterns in immuno-oncology (I-O) compared with conventional therapies and thus may fail to represent the full clinical value associated with disease dynamics and improved survival.

Objective

A cost-effectiveness analysis (CEA) of the I-O Regimen (nivolumab/ipilimumab) versus ipilimumab alone in advanced melanoma was carried out by applying a 5-state partitioned survival model (PSM) as a case study, to explore the I-O treatment response and clinical outcomes. The findings were compared with those of a conventional 3-state PSM.

Materials and Methods

The case study extends the conventional 3-state PSM, by separating the pre-progression state into non-responders and responders, and the post-progression state into normal and I-O progression to account for delayed treatment effects preceding clinical response. Model states were populated using patient-level data (where possible), mapping from the best overall response (BOR), and survival analysis with flexible and traditional parametric methods. Survival functions were applied to progression-free survival (PFS) and overall survival (OS) endpoints across treatment arms using the 4-year follow-up data (data available at the time of the research; since then 5-year follow-up data have been published) from the CheckMate 067 trial. Information on BOR was used as a means of differentiating the I-O treatment response in addition to the outcomes of progression-free and progressed disease. A UK National Health Service and personal social services (NHS/PSS) perspective over a lifetime horizon was used with outcomes discounted at 3.5% annually.

Results

The 5-state PSM generated an increase in quality adjusted life years (QALYs) in both treatment arms and gave a more granular description of patients’ health profiles compared with the traditional 3-state PSM. The incremental QALY increased by 13% (from 2.62 to 2.95 QALYs) and the incremental cost decreased by 12% (£29,125 to £25,678) with the 5-state model. In both models, the Regimen had an incremental cost-effectiveness ratio (ICER) relative to ipilimumab alone within the lower bound of the National Institute for Health and Care Excellence (NICE) reference range (£20,000 per QALY gained).

Conclusion

A 5-state economic model, incorporating relevant I-O health states, can be more informative to gain insight into treatment response and progression differences that are not commonly captured in existing economic models. Clinical trial endpoints, including those relating to treatment response, which are not directly reported in ongoing I-O trials, can be mapped on to the proposed modelled health states (although assumptions are required to do so). Improvements in reporting treatment response in future I-O clinical trials could help to further validate and improve the proposed model framework.

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

BACKGROUND

Due 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.

METHODS

Standard 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.

RESULTS

Spline-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).

CONCLUSIONS

Survival 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 REGISTRATION

JAVELIN Merkel 200 was registered with ClinicalTrials.gov as NCT02155647 on June 4, 2014.

A Case Study Examining the Usefulness of Cure Modelling for the Prediction of Survival Based on Data Maturity

INTRODUCTION

Mixture modelling is increasingly being considered where a potential cure leads to a long life. Traditional methods use relative survival models for frail populations or cure models that have improper survival functions with theoretical infinite lifespans. Additionally, much of the work uses population data with long follow-up or theoretical data for method development.

OBJECTIVE

This case study uses life table data to create a proper survival function in a real-world clinical trial context. In particular, we discuss the impact of the length of trial follow-up on the accuracy of model estimation and the impact of extrapolation to capture long-term survival.

METHODS

A review of recent National Institute for Health and Clinical Excellence (NICE) immuno-oncological and chimeric antigen receptor (CAR) T-cell therapy submissions was performed to assess industry uptake and NICE acceptance of survival analysis methods incorporating the potential for long-term survivorship. The case study analysed a simulated trial-based dataset investigating a curative treatment with long-term mortality based on population life tables. The analysis examined three timepoints corresponding to early trial, end-of-trial follow-up and complete follow-up. Mixture modelling approaches were considered, including both cure modelling and relative survival approaches. The curves were evaluated based on the ability to estimate cure fractions and mean life in years within the time span the models are based on and when extrapolating to capture long-term behaviour. The survival curves were fitted with Weibull distributions using non-mixture and mixture cure models.

RESULTS

The performance of the cure modelling methods depended on the relative maturity of the data, indicating that care is needed when deciding when the methods should be applied. For progression-free survival, the cure fraction simulated was 15%. The cure fractions estimated using the traditional mixture cure model were 43% (95% confidence interval [CI] 30-57) at the first analysis time point (40 months), 15% (95% CI 12-20) at the end-of-study follow-up (153 months) and 0% (95% CI 0-100) at the end of follow-up. Other standard cure modelling methods produced similar results. For overall survival, we observed a similar pattern of goodness of fit, with a good fit for the end-of-study follow-up and poor fit for the other two data cuts. However, in this case, the estimate of the cure fraction was below the true value in the first analysis data.

CONCLUSIONS

This case study suggests cure modelling works well with data in which the disease-specific events have had time to occur. Care is needed when extrapolating from immature data, and further information should support the estimation rather than relying on statistical estimates based on the trial alone.

How Uncertain is the Survival Extrapolation? A Study of the Impact of Different Parametric Survival Models on Extrapolated Uncertainty About Hazard Functions, Lifetime Mean Survival and Cost Effectiveness

BACKGROUND AND OBJECTIVE

The extrapolation of estimated hazard functions can be an important part of cost-effectiveness analyses. Given limited follow-up time in the sample data, it may be expected that the uncertainty in estimates of hazards increases the further into the future they are extrapolated. The objective of this study was to illustrate how the choice of parametric survival model impacts on estimates of uncertainty about extrapolated hazard functions and lifetime mean survival.

METHODS

We examined seven commonly used parametric survival models and described analytical expressions and approximation methods (delta and multivariate normal) for estimating uncertainty. We illustrate the multivariate normal method using case studies based on four representative hypothetical datasets reflecting hazard functions commonly encountered in clinical practice (constant, increasing, decreasing, or unimodal), along with a hypothetical cost-effectiveness analysis.

RESULTS

Depending on the survival model chosen, the uncertainty in extrapolated hazard functions could be constant, increasing or decreasing over time for the case studies. Estimates of uncertainty in mean survival showed a large variation (up to sevenfold) for each case study. The magnitude of uncertainty in estimates of cost effectiveness, as measured using the incremental cost per quality-adjusted life-year gained, varied threefold across plausible models. Differences in estimates of uncertainty were observed even when models provided near-identical point estimates.

CONCLUSIONS

Survival model choice can have a significant impact on estimates of uncertainty of extrapolated hazard functions, mean survival and cost effectiveness, even when point estimates were similar. We provide good practice recommendations for analysts and decision makers, emphasizing the importance of considering the plausibility of estimates of uncertainty in the extrapolated period as a complementary part of the model selection process.

Evaluating Partitioned Survival and Markov Decision-Analytic Modeling Approaches for Use in Cost-Effectiveness Analysis: Estimating and Comparing Survival Outcomes

OBJECTIVE

The objective of this study was to assess long-term survival outcomes for nivolumab and everolimus in renal cell carcinoma predicted by three model structures, a partitioned survival model (PSM) and two variations of a semi-Markov model (SMM), for use in cost-effectiveness analyses.

METHODS

Three economic model structures were developed and populated using parametric curves fitted to patient-level data from the CheckMate 025 trial. Models consisted of three health states: progression-free, progressed disease, and death. The PSM estimated state occupancy using an area under-the-curve approach from overall survival (OS) and progression-free survival (PFS) curves. The SMMs derived transition probabilities to calculate patient flow between health states. One SMM assumed that post-progression survival (PPS) was independent of PFS duration (PPS Markov); the second SMM assumed differences in PPS based on PFS duration (PPS-PFS Markov).

RESULTS

All models provide a reasonable fit to the observed OS data at 2 years. For estimating cost effectiveness, however, a more relevant comparison is between estimates of OS over the modeling horizon, because this will likely impact differences in costs and quality-adjusted life-years. Estimates of the incremental mean survival benefit of nivolumab versus everolimus over 20 years were 6.6 months (PSM), 7.6 months (PPS Markov), and 7.4 months (PPS-PFS Markov), reflecting non-trivial differences of + 14% and + 11%, respectively, compared with PSM.

CONCLUSIONS

The evidence from this study and previous work highlights the importance of the assumptions underlying any model structure, and the need to validate assumptions regarding survival and the application of treatment effects against what is known about the characteristics of the disease.

A Review of Recent Decision-Analytic Models Used to Evaluate the Economic Value of Cancer Treatments

Cost-effectiveness analysis provides information on the potential value of new cancer treatments, which is particularly pertinent for decision makers as demand for treatment grows while healthcare budgets remain fixed. A range of decision-analytic modelling approaches can be used to estimate cost effectiveness. This study summarises the key modelling approaches considered in oncology, alongside their advantages and limitations. A review was conducted to identify single technology appraisals (STAs) submitted to the National Institute for Health and Care Excellence (NICE) and published papers reporting full economic evaluations of cancer treatments published within the last 5 years. The review was supplemented with the existing methods literature discussing cancer modelling. In total, 100 NICE STAs and 124 published studies were included. Partitioned-survival analysis (n = 54) and discrete-time state transition structures (n = 41) were the main structures submitted to NICE. Conversely, the published studies reported greater use of discrete-time state transition models (n = 102). Limited justification of model structure was provided by authors, despite an awareness in the existing literature that the model structure should be considered thoroughly and can greatly influence cost-effectiveness results. Justification for the choice of model structure was limited and studies would be improved with a thorough rationale for this choice. The strengths and weaknesses of each approach should be considered by future researchers. Alternative methods (such as multi-state modelling) are likely to be utilised more frequently in the future, and so justification of these more advanced methods is paramount to their acceptability to inform healthcare decision making.

Tumor treating fields and maintenance temozolomide for newly-diagnosed glioblastoma: a cost-effectiveness study

Purpose: The EF-14 trial demonstrated that adding tumor treating fields (TTFields) to maintenance temozolomide (TMZ) significantly extends progression-free survival (PFS) and overall survival (OS) for newly-diagnosed glioblastoma (GBM) patients. This study assessed the cost-effectiveness of TTFields and TMZ for newly-diagnosed GBM from the US healthcare system perspective. Methods and materials: Outcomes for newly-diagnosed GBM patients were estimated over a lifetime horizon using an area under the curve model with three states: stable disease, progressive disease, or death. The survival model integrated the 5-year EF-14 trial results with long-term GBM epidemiology data and US background mortality rates. Adverse event rates were derived from the EF-14 trial data. Utility values to determine quality-adjusted life-years, adverse event costs, and supportive care costs were obtained from published literature. A 3% discount rate was applied to future costs and outcomes. One-way and probabilistic sensitivity analyses were performed to assess result uncertainty due to parameter variability. Results: Treatment with TTFields and TMZ was estimated to result in a mean increase in survival of 1.25 life years (95% credible range [CR] = 0.89-1.67) and 0.96 quality-adjusted life years (QALYs) (95% CR = 0.67-1.30) compared to treatment with TMZ alone. The incremental total cost was $188,637 (95% CR = $145,324-$225,330). The incremental cost-effectiveness ratio (ICER) was $150,452 per life year gained and $197,336 per QALY gained. The model was most sensitive to changes in the cost of TTFields treatment. Conclusions: Adding TTFields to maintenance TMZ resulted in a substantial increase in the estimated mean lifetime survival and quality-adjusted survival for newly-diagnosed GBM patients. Treatment with TTFields can be considered cost-effective within the reported range of willingness-to-pay thresholds in the US.

Generalized Linear Models for Flexible Parametric Modeling of the Hazard Function

Background. Parametric modeling of survival data is important, and reimbursement decisions may depend on the selected distribution. Accurate predictions require sufficiently flexible models to describe adequately the temporal evolution of the hazard function. A rich class of models is available among the framework of generalized linear models (GLMs) and its extensions, but these models are rarely applied to survival data. This article describes the theoretical properties of these more flexible models and compares their performance to standard survival models in a reproducible case study. Methods. We describe how survival data may be analyzed with GLMs and their extensions: fractional polynomials, spline models, generalized additive models, generalized linear mixed (frailty) models, and dynamic survival models. For each, we provide a comparison of the strengths and limitations of these approaches. For the case study, we compare within-sample fit, the plausibility of extrapolations, and extrapolation performance based on data splitting. Results. Viewing standard survival models as GLMs shows that many impose a restrictive assumption of linearity. For the case study, GLMs provided better within-sample fit and more plausible extrapolations. However, they did not improve extrapolation performance. We also provide guidance to aid in choosing between the different approaches based on GLMs and their extensions. Conclusions. The use of GLMs for parametric survival analysis can outperform standard parametric survival models, although the improvements were modest in our case study. This approach is currently seldom used. We provide guidance on both implementing these models and choosing between them. The reproducible case study will help to increase uptake of these models.

Survival Analysis in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab

Introduction

Complex underlying risk functions associated with immuno-oncology treatments have led to exploration of different methods (parametric survival, spline, landmark, and cure-fraction models) to estimate long-term survival outcomes. The objective of this study was to examine differences in estimated short- and long-term survival in previously treated metastatic Merkel cell carcinoma (mMCC) patients receiving avelumab, when using alternative extrapolation approaches.

Methods

Efficacy data from the phase 2 JAVELIN Merkel 200 trial (part A) with at least 12 months of follow-up were analyzed. Standard parametric survival analyses and analyses of overall survival (OS) as a function of surrogate outcomes comprised of response (landmark analyses) and progression-free survival plus post-progression survival (PFS + PPS) were used to project OS. Overall survival throughout lifetime was projected and compared with the observed OS data with at least 24 months of follow-up.

Results

Estimated OS from all three approaches provided a good fit to the observed OS curve from at least 12 months of follow-up. However, performance compared with OS data from at least 24 months showed that the landmark approach followed by PFS + PPS provided a better fit to the data as compared to standard parametric analysis. Mean life expectancy estimated with avelumab was 2.48 years with best-fitting parametric function (a log-normal distribution), 3.15 years with the landmark approach, and 3.54 years with PFS + PPS.

Conclusion

Although standard parametric survival analysis may provide a good fit to short-term survival, it appears to underestimate the long-term survival benefits associated with avelumab in mMCC. Extrapolations based on surrogate outcomes of response or progression predict OS outcomes from longer follow-up better and appear to provide more clinically plausible projections.

Funding

EMD Serono Inc, Rockland, MA, a business of Merck KGaA, Darmstadt, Germany.

Electronic Supplementary Material

The online version of this article (10.1007/s12325-019-01034-0) contains supplementary material, which is available to authorized users.

Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations

BACKGROUND

Standard parametric survival models are commonly used to estimate long-term survival in oncology health technology assessments; however, they can inadequately represent the complex pattern of hazard functions or underlying mechanism of action (MoA) of immuno-oncology (IO) treatments.

OBJECTIVE

The aim of this study was to explore methods for extrapolating overall survival (OS) and provide insights on model selection in the context of the underlying MoA of IO treatments.

METHODS

Standard parametric, flexible parametric, cure, parametric mixture and landmark models were applied to data from ATLANTIC (NCT02087423; data cut-off [DCO] 3 June 2016). The goodness of fit of each model was compared using the observed survival and hazard functions, together with the plausibility of corresponding model extrapolation beyond the trial period. Extrapolations were compared with updated data from ATLANTIC (DCO 7 November 2017) for validation.

RESULTS

A close fit to the observed OS was seen with all models; however, projections beyond the trial period differed. Estimated mean OS differed substantially across models. The cure models provided the best fit for the new DCO.

CONCLUSIONS

Standard parametric models fitted to the initial ATLANTIC DCO generally underestimated longer-term OS, compared with the later DCO. Cure, parametric mixture and response-based landmark models predicted that larger proportions of patients with metastatic non-small cell lung cancer receiving IO treatments may experience long-term survival, which was more in keeping with the observed data. Further research using more mature OS data for IO treatments is needed.

Cohort versus patient level simulation for the economic evaluation of single versus combination immuno-oncology therapies in metastatic melanoma

Background: Model structure, despite being a key source of uncertainty in economic evaluations, is often not treated as a priority for model development. In oncology, partitioned survival models (PSMs) and Markov models, both types of cohort model, are commonly used, but patient responses to newer immuno-oncology (I-O) agents suggest that more innovative model frameworks should be explored. Objective: A discussion of the theoretical pros and cons of cohort level vs patient level simulation (PLS) models provides the background for an illustrative comparison of I-O therapies, namely nivolumab/ipilimumab combination and ipilimumab alone using patient level data from the CheckMate 067 trial in metastatic melanoma. PSM, Markov, and PLS models were compared on the basis of coherence with short-term clinical trial endpoints and long-term cost per QALY outcomes reported. Methods: The PSM was based on Kaplan-Meier curves from CheckMate 067 with 3-year data on progression free survival (PFS) and overall survival (OS). The Markov model used time independent transition probabilities based on the average trajectory of PFS and OS over the trial period. The PLS model was developed based on baseline characteristics hypothesized to be associated with disease as well as significant mortality and disease progression risk factors identified through a proportional hazards model. Results: The short-term Markov model outputs matched the 1-3 year clinical trial results approximately as well as the PSMs for OS but not PFS. The fixed (average) cohort PLS results corresponded as well as the PSMs for OS in the combination therapy arm and PFS in the monotherapy arm. Over the lifetime horizon, the PLS produced an additional 5.95 quality adjusted life years (QALYs) associated with combination therapy relative to ipilimumab alone, resulting in an incremental cost-effectiveness ratio (ICER) of £6,474 per QALY, compared with £14,194 for the PSMs which gave an incremental benefit of between 2.2 and 2.4 QALYs. The Markov model was an outlier (∼ £49,000 per QALY in the base case). Conclusions: The 4- and 5-state versions of the PSM cohort model estimated in this study deviate from the standard 3-state approach to better capture I-O response patterns. Markov and PLS approaches, by modeling state transitions explicitly, could be more informative in understanding I-O immune response, the PLS particularly so by reflecting heterogeneity in treatment response. However, both require a number of assumptions to capture the immune response effectively. Better I-O representation with surrogate endpoints in future clinical trials could yield greater model validity across all models.

Heterogeneous Recommendations for Oncology Products Among Different HTA Systems: A Comparative Assessment

Rising budget constraints and demands for healthcare services create additional complexity within the decision process for resource allocation. Innovations and scientific progress have been shown to be key drivers of the increase in healthcare expenditures (1). In the context of rising medical care costs and limited resources, Health Technology Assessment (HTA) was developed as a tool to inform decision-making and to provide the rationalization behind these decisions driving resource allocation and spending for health technology products. Furthermore, HTA agencies make the decision-making process more transparent. The HTA approach involves evaluating multiple aspects of a new product's value in order to maximize health gain provided within the setting of limited resources.

Advanced therapy medicinal products and health technology assessment principles and practices for value-based and sustainable healthcare

BACKGROUND

Advanced therapy medicinal products (ATMPs) are beginning to reach European markets, and questions are being asked about their value for patients and how healthcare systems should pay for them.

OBJECTIVES

To identify and discuss potential challenges of ATMPs in view of current health technology assessment (HTA) methodology-specifically economic evaluation methods-in Europe as it relates to ATMPs, and to suggest potential solutions to these challenges.

METHODS

An Expert Panel reviewed current HTA principles and practices in relation to the specific characteristics of ATMPs.

RESULTS

Three key topics were identified and prioritised for discussion-uncertainty, discounting, and health outcomes and value. The panel discussed that evidence challenges linked to increased uncertainty may be mitigated by collection of follow-on data, use of value of information analysis, and/or outcomes-based contracts. For discount rates, an international, multi-disciplinary forum should be established to consider the economic, social and ethical implications of the choice of rate. Finally, consideration of the feasibility of assessing the value of ATMPs beyond health gain may also be key for decision-making.

CONCLUSIONS

ATMPs face a challenge in demonstrating their value within current HTA frameworks. Consideration of current HTA principles and practices with regards to the specific characteristics of ATMPs and continued dialogue will be key to ensuring appropriate market access.

CLASSIFICATION CODE

I.

Survival Extrapolation in Cancer Immunotherapy: A Validation-Based Case Study

BACKGROUND

Immune-checkpoint inhibitors may provide long-term survival benefits via a cured proportion, yet data are usually insufficient to prove this upon submission to health technology assessment bodies.

OBJECTIVE

We revisited the National Institute for Health and Care Excellence assessment of ipilimumab in melanoma (TA319). We used updated data from the pivotal trial to assess the accuracy of the extrapolation methods used and compared these to previously unused techniques to establish whether an alternative extrapolation may have provided more accurate survival projections.

METHODS

We compared projections from the piecewise survival model used in TA319 and those produced by alternative models (fit to trial data with minimum follow-up of 3 years) to a longer-term data cut (5-year follow-up). We also compared projections to external data to help assess validity. Alternative approaches considered were parametric, spline-based, mixture, and mixture-cure models.

RESULTS

Only the survival model used in TA319 and a mixture-cure model provided 5-year survival predictions close to those observed in the 5-year follow-up data set. Standard parametric, spline, and non-curative-mixture models substantially underestimated 5-year survival. Survival estimates from the TA319 model and the mixture-cure model diverge considerably after 5 years and remain unvalidated.

CONCLUSIONS

In our case study, only models that incorporated an element of external information (through a cure fraction combined with background mortality rates or using registry data) provided accurate estimates of 5-year survival. Flexible models that were able to capture the complex hazard functions observed during the trial, but which did not incorporate external information, extrapolated poorly.

A review and validation of overall survival extrapolation in health technology assessments of cancer immunotherapy by the National Institute for Health and Care Excellence: how did the initial best estimate compare to trial data subsequently made available?

BACKGROUND

Validation of overall survival (OS) extrapolations of immune-checkpoint inhibitors (ICIs) during the National Institute for Health and Care Excellence (NICE) Single Technology Assessment (STA) process is limited due to data still maturing at the time of submission. Inaccurate extrapolation may lead to inappropriate decision-making. The availability of more mature trial data facilitates a retrospective analysis of the plausibility and validity of initial extrapolations. This study compares these extrapolations to subsequently available longer-term data.

METHODS

A systematic search of completed NICE appraisals of ICIs from March 2000 to December 2017 was performed. A targeted search was also undertaken to procure published OS data from the pivotal clinical trials for each identified STA made available post-submission to NICE. Initial Kaplan-Meier curves and associated extrapolations from NICE documentation were extracted to compare the accuracy of OS projections versus the most mature data.

RESULTS

The review identified 11 STAs, of which 10 provided OS data upon submission to NICE. The extrapolations undertaken considered parametric or piecewise survival models. Additional data cut-offs provided a mean of 18 months of OS beyond the end of the original data. Initial extrapolations typically under-estimated OS from the most mature data cut-off by 0.4-2.7%, depending on the choice of assessment method and use of the manufacturer- or ERG-preferred extrapolation.

CONCLUSION

Long-term extrapolation of OS is required for NICE STAs based on initial immature OS data. The results of this study demonstrate that the initial OS extrapolations employed by manufacturers and ERGs generally predicted OS reasonably well when compared to more mature data (when available), although on average they appeared to underestimate OS. This review and validation shows that, while the choice of OS extrapolation is uncertain, the methods adopted are generally aligned with later-published follow-up data and appear appropriate for informing HTA decisions.

Oncology from an HTA and Health Economic Perspective

In this chapter, we will present and discuss the challenges of assessing oncology products from a health economic perspective. We will provide a brief introduction on the need for economic evaluation in health care and focus on cost-effectiveness and comparative aspects of the evaluation of oncology products, which are of paramount interest to HTA decision-making bodies using economic evaluation in their decision-making framework. As the burden of oncology is well-documented, we do not discuss it in detail here. Before we address the specific issue of oncology, we will briefly define the critical aspects of HTA assessment and also define what a cost-effectiveness analysis is and why economic modelling is the most appropriate tool to assess the cost-effectiveness of oncology products. We will touch upon the prices of oncology drugs and the questions that high prices raise regarding funding and availability. We then present an overview of the general structure of an oncology cost-effectiveness model. Usually, this is quite simple, representing response, progression, advanced-stage disease and death. Despite the relative simplicity of these models, some issues may render the evaluation more complex; we will touch upon these in this chapter: Issue with clinical inputs due to the design of randomised clinical trials (e.g. cross-over designs involving a treatment switch) Need for survival extrapolation and limitations of current parametric models Rare conditions with limited economic and comparative evidence available High pace of clinical development Finally, we will conclude with a discussion of the uncertainty around the evaluation of oncology products and the major evolution expected in health economics in oncology.

Using Evidence from Randomised Controlled Trials in Economic Models: What Information is Relevant and is There a Minimum Amount of Sample Data Required to Make Decisions?

Evidence from randomised controlled trials (RCTs) is used to support regulatory approval and reimbursement decisions. I discuss how these decisions are typically made and argue that the amount of sample data and regulatory authorities' concerns over multiplicity are irrelevant when making reimbursement decisions. Decision analytic models (DAMs) are usually necessary to meet the requirements of an economic evaluation. DAMs involve inputs relating to health benefits and resource use that represent unknown true population parameters. Evidence about parameters may come from a variety of sources, including RCTs, and uncertainty about parameters is represented by their joint posterior distribution. Any impact of multiplicity is mitigated through the prior distribution. I illustrate my perspective with three examples: the estimation of a treatment effect on a rare event; the number of RCTs available in a meta-analysis; and the estimation of population mean overall survival. I conclude by recommending that reimbursement decisions should be followed by an assessment of the value of sample information and the DAM revised structurally as necessary and to include any new sample data that may be generated.

Modeling the economic outcomes of immuno-oncology drugs: alternative model frameworks to capture clinical outcomes

BACKGROUND

Economic models in oncology are commonly based on the three-state partitioned survival model (PSM) distinguishing between progression-free and progressive states. However, the heterogeneity of responses observed in immuno-oncology (I-O) suggests that new approaches may be appropriate to reflect disease dynamics meaningfully.

MATERIALS AND METHODS

This study explored the impact of incorporating immune-specific health states into economic models of I-O therapy. Two variants of the PSM and a Markov model were populated with data from one clinical trial in metastatic melanoma patients. Short-term modeled outcomes were benchmarked to the clinical trial data and a lifetime model horizon provided estimates of life years and quality adjusted life years (QALYs).

RESULTS

The PSM-based models produced short-term outcomes closely matching the trial outcomes. Adding health states generated increased QALYs while providing a more granular representation of outcomes for decision making. The Markov model gave the greatest level of detail on outcomes but gave short-term results which diverged from those of the trial (overstating year 1 progression-free survival by around 60%).

CONCLUSION

Increased sophistication in the representation of disease dynamics in economic models is desirable when attempting to model treatment response in I-O. However, the assumptions underlying different model structures and the availability of data for health state mapping may be important limiting factors.