An Evaluation of Survival Curve Extrapolation Techniques Using Long-Term Observational Cancer Data

Abstract

Objectives. Uncertainty in survival prediction beyond trial follow-up is highly influential in cost-effectiveness analyses of oncology products. This research provides an empirical evaluation of the accuracy of alternative methods and recommendations for their implementation. Methods. Mature (15-year) survival data were reconstructed from a published database study for “no treatment,” radiotherapy, surgery plus radiotherapy, and surgery in early stage non–small cell lung cancer in an elderly patient population. Censored data sets were created from these data to simulate immature trial data (for 1- to 10-year follow-up). A second data set with mature (9-year) survival data for no treatment was used to extrapolate the predictions from models fitted to the first data set. Six methodological approaches were used to fit models to the simulated data and extrapolate beyond trial follow-up. Model performance was evaluated by comparing the relative difference in mean survival estimates and the absolute error in the difference in mean survival v. the control with those from the original mature survival data set. Results. Model performance depended on the treatment comparison scenario. All models performed reasonably well when there was a small short-term treatment effect, with the Bayesian model coping better with shorter follow-up times. However, in other scenarios, the most flexible Bayesian model that could be estimated in practice appeared to fit the data less well than the models that used the external data separately. Where there was a large treatment effect (hazard ratio = 0.4), models that used external data separately performed best. Conclusions. Models that directly use mature external data can improve the accuracy of survival predictions. Recommendations on modeling strategies are made for different treatment benefit scenarios.

Extrapolating Parametric Survival Models in Health Technology Assessment: A Simulation Study

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How Sensitive is Sensitivity Analysis?: Evaluation of Pharmacoeconomic Submissions in Korea

“…According to Kim and Prasad (2015), who followed up on the survival improvement of drugs approved based on the surrogate endpoint at the time of FDA approval (median follow-up 4.4 years), only 5 out of 36 cases demonstrated survival gain ( Kim and Prasad, 2015 ). However, few efforts have been made to assess the validity of survival predictions compared to actual data ( Latimner, 2013 ; Vickers, 2019 ).…”

Do Older Patients with Stage IB Non-Small-Cell Lung Cancer Obtain Survival Benefit from Surgery? A Propensity Score Matching Study Based on the SEER Database

“…Therefore, it is controversial whether the clinical bene ts of surgical treatment are signi cant for older patients even if their NSCLC is at an early stage, such as stage IB. Although there have been a few studies exploring the advantages and disadvantages of surgical treatment in older patients with early-stage NSCLC, these studies have the insu cient sample size and interference of confounding factors [18,19]. Our results showed that there was a severely unbalanced distribution of clinicopathological characteristics between the surgical and non-surgical groups, although surgical treatment was the most signi cant protective factor among all the clinical factors for both OS and DSS (Figure S1-2, Table 1).…”