Correcting for non-compliance of repeated binary outcomes in randomized clinical trials: randomized analysis approach.
We develop the randomized analysis for repeated binary outcomes with non-compliance. A break randomization-based semi-parametric estimation procedure for both the causal risk difference and the causal risk ratio is proposed for repeated binary data. Although we assume the simple structural models for potential outcomes, we choose to avoid making any assumptions about comparability beyond those implied by randomization at time zero. The proposed methods can incorporate non-compliance information, while preserving the validity of the test of the null hypothesis, and even in the presence of non-random non-compliance can give the estimate of the causal effect that treatment would have if all individuals complied with their assigned treatment. The methods are applied to data from a randomized clinical trial for reduction of febrile neutropenia events among acute myeloid leukaemia patients, in which a prophylactic use of macrophage colony-stimulating factor (M-CSF) was compared to placebo during the courses of intensive chemotherapies.
Matsuyama
Yutaka
Y
Biostatistics/Epidemiology and Preventive Health Sciences, School of Health Sciences and Nursing, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan. matuyama@pbh.med.kyoto-u.ac.jp
D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D007951:Leukemia, Myeloid / Q000188:drug therapy; D016173:Macrophage Colony-Stimulating Factor / Q000627:therapeutic use; D008954:Models, Biological; D009503:Neutropenia / Q000517:prevention & control; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016312:Treatment Refusal
D016173:Macrophage Colony-Stimulating Factor