Optimizing the Analysis of Adherence Interventions Using Logistic Generalized Estimating Equations

Interventions aimed at improving HIV medication adherence could be dismissed as ineffective due to statistical methods that are not sufficiently sensitive. Cross-sectional techniques such as t tests are common to the field, but potentially inaccurate due to increased risk of chance findings and invalid assumptions of normal distribution. In a secondary analysis of a randomized controlled trial, two approaches using logistic generalized estimating equations (GEE)—planned contrasts and growth curves—were examined for evaluating percent adherence data. Results of the logistic GEE approaches were compared to classical analysis of variance (ANOVA). Robust and bootstrapped estimation was used to obtain empirical standard error estimates. Logistic GEE with either planned contrasts or growth curves in combination with robust standard error estimates was superior to classical ANOVA for detecting intervention effects. The choice of longitudinal model led to key differences in inference. Implications and recommendations for applied researchers are discussed.