A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes.

A frequently addressed issue in clinical trials is the comparison of censored paired survival outcomes, for example, when individuals were matched based on their characteristics prior to the analysis. In this regard, a proper incorporation of the dependence structure of the paired censored outcomes is required and, up to now, appropriate methods are only rarely available in the literature. Moreover, existing methods are not motivated by the strive for insights by means of an easy-to-interpret parameter. Hence, we seek to develop a new estimand-driven method to compare the effectiveness of two treatments in the context of right-censored survival data with matched pairs. With the help of competing risks techniques, the so-called relative treatment effect is estimated. This estimand describes the probability that individuals under Treatment 1 have a longer lifetime than comparable individuals under Treatment 2. We derive hypothesis tests and confidence intervals based on a studentized version of the estimator, where resampling-based inference is established by means of a randomization method. In a simulation study, we demonstrate for numerous sample sizes and different amounts of censoring that the developed test exhibits a good power. Finally, we apply the methodology to a well-known benchmark data set from a trial with patients suffering from diabetic retinopathy.
(© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)