Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials (2017)

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One of the basic and important ideas about the design of randomized controlled trials (RCT) and the analysis of the data will be that a group of patients to which a new drug was administered is never homogeneous in terms of response to the drug. In response to the drug, some patients improve, some patients experience no change and other patients even worsen contrary to patients’ expectation. These phenomena can be observed even in patients assigned to the placebo group. Namely, nobody knows whether or not these changes are really caused by the drug administered. To deal with these unpredictable heterogeneities of the subject-specific response, the generalized linear mixed (or mixed-effects) models are known to provide a flexible and powerful tool where the unpredictable subject-specific responses are assumed to be unobserved random-effects variables.



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