197-30: Analyzing Incomplete Binary Repeated Measures Data Using SAS®
نویسندگان
چکیده
This paper presents a case study in longitudinal data analysis where the goal is to estimate the efficacy of a new drug for treatment of MDD. Data characteristic indicate: 1. Subjects from different treatment groups drop out differentially across time. 2. There are a high proportion of subjects who never experience any response. To overcome these challenges, we developed a logistic random-effects model with random intercepts. While the model is specified conditionally on subject random effect variable, we also draw inferences on population-averaged important to the assessment of the treatments’ efficacy in a population. Specifically, we present and describe using SAS Proc NLMIXED and %GLIMMIX macro to fit the logistic randomeffects model.
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