Key points are not available for this paper at this time.
Exploration of the variation of treatment effect over time in randomized clinical trials with low event rates is limited by lack of power. A meta-analysis on individual patient data from such trials can partly solve the problem, but brings other computational difficulties. Using an example in hypertension, we describe appropriate methods for graphical description and statistical modelling of treatment-time interactions in large data sets. Also, a method is developed for determining the total number of events required to detect treatment-period interactions of plausible magnitude. We conclude that trialists tend to overinterpret the observed data when looking for potential treatment-time interactions by visual comparisons of survival curves, failing to realize the substantial amounts of data that are needed for their detection and estimation.
Boutitie et al. (Wed,) studied this question.