One-step validation method for surrogate endpoints using data from multiple randomized cancer clinical trials with failure-time endpoints

Abstract

The search for valid surrogate endpoints remains as topical as ever for accelerated effectively the phase 3 trials. Numerous validation methods have been proposed with the most popular used in a context of meta-analysis, based on a two-step analysis strategy. For two failure time endpoints, two association measurements are usually considered, the Kendall's τ at the individual level and the adjusted R2 (adjR2) at the trial level. However, adjR2 is not always available mainly due to model estimation constraints. We propose a one-step validation approach based on a joint frailty model, including both individual-level and trial-level random effects. Parameters have been estimated using a semi-parametric penalized marginal log-likelihood method, and various numerical integration approaches were considered. Both individual and trial-level surrogacy was evaluated using a new definition of Kendall's τ and the coefficient of determination. Estimators' performances were evaluated using simulation studies and satisfactory results were found. The model was applied to individual patient data meta-analyses in gastric cancer to assess disease-free survival (DFS) as a surrogate for overall survival (OS), as part of the evaluation of adjuvant therapy. We popularize this new surrogate endpoints validation approach by making the method available in a user-friendly R package. Thus, we provided in the frailtypack R package numerous tools, including more flexible functions, for the validation of candidate surrogate endpoints, using data from multiple randomized clinical trials.