Comparison of predictor approaches for longitudinal binary outcomes: application to anesthesiology data

Abstract

Longitudinal data with binary repeated responses are now widespread among clinical studies and standard statistical analysis methods have become inadequate in the answering of clinical hypotheses. Instead of such conventional approaches, statisticians have started proposing better techniques, such as the Generalized Estimating Equations (GEE) approach and Generalized Linear Mixed Models (GLMM) technique. In this research, we undertook a comparative study of modeling binary repeated responses using an anesthesiology dataset which has 375 patient data with clinical variables. We modeled the relationship between hypotension and age, gender, surgical department, positions of patients during surgery, diastolic blood pressure, pulse, electrocardiography and doses of Marcain-heavy, chirocaine, fentanyl, and midazolam. Moreover, parameter estimates between the GEE and the GLMM were compared. The parameter estimates, except time-after, Marcain-Heavy, and Fentanyl from the GLMM, are larger than those from GEE. The standard errors from the GLMM are larger than those from GEE. GLMM appears to be more suitable approach than the GEE approach for the analysis hypotension during spinal anesthesia.

Keywords

• Generalized estimating equations
• Generalized linear mixed models
• Longitudinal data