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A method for the analysis of repeated binary outcomes in randomized clinical trials with non‐compliance
Author(s) -
Sato Tosiya
Publication year - 2001
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/sim.741
Subject(s) - estimator , statistics , missing data , binary data , absolute risk reduction , mathematics , randomization , causal inference , randomized controlled trial , binary number , econometrics , computer science , confidence interval , medicine , arithmetic , surgery
When analysing repeated binary data from randomized trials, the model‐based approaches, such as generalized estimating equations, are frequently used. Such methods ignore compliance information and give the model‐based intention‐to‐treat estimate of treatment effect. In this paper, the design‐based (randomization‐based) semi‐parametric estimation procedure is given in the estimation of causal risk difference. The resulting risk difference estimator is interpreted as an extension of the instrumental variables estimator for a binary outcome which has the causal interpretation. Extension of the proposed method to stratified analysis is given for data from stratified randomization or meta‐analysis. It yields a Mantel–Haenszel type risk difference estimator. As a special case of stratified analysis, the pattern mixture model which stratifies the data by pattern of missing data is performed. Application of the proposed method to a trial in which endpoints were the occurrences of fever over three courses is provided. The same ideas are applied to the causal risk ratio estimation. Copyright © 2001 John Wiley & Sons, Ltd.