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Assessing treatment–time interaction in clinical trials with time to event data: a meta‐analysis of hypertension trials
Author(s) -
Boutitie F.,
Gueyffier F.,
Pocock S. J.,
Boissel J. P.
Publication year - 1998
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19981230)17:24<2883::aid-sim900>3.0.co;2-l
Subject(s) - computer science , clinical trial , event data , event (particle physics) , meta analysis , statistical power , treatment effect , statistics , data mining , econometrics , medicine , mathematics , physics , quantum mechanics , analytics , traditional medicine
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. © 1998 John Wiley & Sons, Ltd.