  Delayed separation of survival curves is a common occurrence in confirmatory
studies in immuno-oncology. Many novel statistical methods that aim to
efficiently capture potential long-term survival improvements have been
proposed in recent years. However, the vast majority do not consider
stratification, which is a major limitation considering that most (if not all)
large confirmatory studies currently employ a stratified primary analysis. In
this article, we combine recently proposed weighted log-rank tests that have
been designed to work well under a delayed separation of survival curves, with
stratification by a baseline variable. The aim is to increase the efficiency of
the test when the stratifying variable is highly prognostic for survival. As
there are many potential ways to combine the two techniques, we compare several
possibilities in an extensive simulation study. We also apply the techniques
retrospectively to two recent randomized clinical trials.
