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 Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment

Background: External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare. Purpose: This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA. Data Sources: Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking. Study Selection: Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation. Data Extraction: Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods. Data Synthesis: Across 18 methods identified from 22 studies, themes included use of informative prior(s) (n = 5), piecewise (n = 7), and general population adjustment (n = 9), plus a variety of “other” (n = 8) approaches. Most methods were applied in cancer populations (n = 13). No studies compared or validated their method against another method that also incorporated external evidence. Limitations: As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review. Conclusions: Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research. This review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment. We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of “other” approaches. No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable

A comparison of partitioned survival analysis and state transition multi-state modelling approaches using a case study in oncology

Aims To construct and compare a partitioned-survival analysis (PartSA) and a semi-Markov multi-state model (MSM) to investigate differences in estimated cost effectiveness of a novel cancer treatment from a UK perspective. Materials and Methods Data from a cohort of late-stage cancer patients (N > 700) enrolled within a randomized, controlled trial were used to populate both modelling approaches. The statistical software R was used to fit parametric survival models to overall survival (OS) and progression-free survival (PFS) data to inform the PartSA (package “flexsurv”). The package “mstate” was used to estimate the MSM transitions (permitted transitions: (T1) “progression-free” to “dead”, (T2) “post-progression” to “death”, and (T3) “pre-progression” to “post-progression”). Key costs included were treatment-related (initial, subsequent, and concomitant), adverse events, hospitalizations and monitoring. Utilities were stratified by progression. Outcomes were discounted at 3.5% per annum over a 15-year time horizon. Results The PartSA and MSM approaches estimated incremental cost-effectiveness ratios (ICERs) of £342,474 and £411,574, respectively. Scenario analyses exploring alternative parametric forms provided incremental discounted life-year estimates that ranged from +0.15 to +0.33 for the PartSA approach, compared with −0.13 to +0.23 for the MSM approach. This variation was reflected in the range of ICERs. The PartSA produced ICERs between £234,829 and £522,963, whereas MSM results were more variable and included instances where the intervention was dominated and ICERs above £7 million (caused by very small incremental QALYs). Limitations and conclusions Structural uncertainty in economic modelling is rarely explored due to time and resource limitations. This comparison of structural approaches indicates that the choice of structure may have a profound impact on cost-effectiveness results. This highlights the importance of carefully considered model conceptualization, and the need for further research to ascertain when it may be most appropriate to use each approach

cost effectiveness ce of avelumab vs standard care sc for the treatment of patients pts with metastatic merkel cell carcinoma mmcc

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