Analyzing incomplete binary repeated measures data using SAS®
(2005)5400<m:linebreak></m:linebreak>Mixed models; Regressió logística; SAS; Dades longitudinals.Abstract
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 random effects model.