A method for the analysis of repeated binary outcomes in randomized clinical trials with non-compliance.
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.
Sato
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Department of Epidemiology and Biostatistics, Kyoto University School of Public Health, Yoshida Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan. shun@pbh.med.kyoto_u.ac.jp
D000971:Antineoplastic Combined Chemotherapy Protocols / Q000009:adverse effects / Q000627:therapeutic use; D004311:Double-Blind Method; D005334:Fever / Q000517:prevention & control; D006801:Humans; D007951:Leukemia, Myeloid / Q000188:drug therapy; D016173:Macrophage Colony-Stimulating Factor / Q000008:administration & dosage; D008432:Mathematical Computing; D015337:Multicenter Studies as Topic; D009503:Neutropenia / Q000517:prevention & control; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*; D016312:Treatment Refusal
D016173:Macrophage Colony-Stimulating Factor