Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R

In clinical trials, surrogate endpoints are often used instead of true endpoints due to practical convenience, time efficiency, and reduced sample size requirements. To determine if a surrogate endpoint can effectively replace a true endpoint, a meta-analytic approach is often employed, evaluating the surrogate endpoint at both the individual and trial levels. Ideally, both surrogacy levels should be sufficiently high to consider the surrogate endpoint a valid replacement for purposes such as accelerated regulatory approval. We aimed to provide an R implementation for surrogacy evaluation of categorical, survival, or continuous surrogate endpoints for a survival true endpoint.

In settings where the true endpoint is a time-to-event variable and the surrogate endpoint is continuous, categorical, or also a time-to-event endpoint, the meta-analytic approach to evaluate surrogacy is based on a two-stage approach. In the first stage, a copula model is employed and a measure for individual level surrogacy is estimated. In the second stage, the estimates of the treatment effects are used to compute a measure for trial-level surrogacy. At this step, measurement error can be taken into account via weights or via a model.

The R package Surrogate provides functions that implement the two-stage approach for various settings. For all settings, models incorporating Clayton, Plackett and Hougaard copulas are available. Additionally, the functions allow for adjustments for measurement error. We demonstrate their use across these settings by applying them to various datasets.

We demonstrate how the R package Surrogate can be used to evaluate categorical, survival, or continuous surrogate endpoints for a survival true endpoint.