Generalized Estimating Equations for Missing Data


chartquestionmarkThis article in Human Psychopharmacology: Clinical and Experimental, written by Sterling McPherson, Celestina Barbosa-Leiker, Michael McDonell, and colleagues from Washington State University in the Pacific Northwest Node, reports on an analysis of data from the National Drug Abuse Treatment Clinical Trials Network buprenorphine protocol CTN-0003.

Many current procedures for handling missing data in clinical trials can result in inaccurate results from the study. This article proposes another method, using multiple imputation and generalized estimating equations.

In this study, listwise deletion (i.e., using complete cases only), positive urine analysis (UA) imputation, and multiple imputation (MI) were used to evaluate the effect of baseline substance use and buprenorphine/naloxone tapering schedule (7 or 28 days) on the probability of a positive UA (UA+) across the 4-week treatment period. The listwise deletion generalized estimating equations (GEE) model demonstrated that those in the 28-day taper group were less likely to submit a UA+ for opioids during the treatment period, as did the positive UA imputation model. The MI model also demonstrated a similar effect of taper group, but the effect size was more similar to that of the listwise deletion model.

Conclusions: The missing data situation described in this investigation generalizes to many other substance use psychopharmacology clinical trials wherein there is missing data on the outcome of interest only. Future researchers may find utilization of the MI procedure in conjunction with the common method of GEE analysis as a helpful analytic approach when the missing at random assumption is justifiable.

Citation: McPherson S, Barbosa-Leiker C, McDonell M, et al. Longitudinal missing data strategies for substance use clinical trials using generalized estimating equations: An example with a buprenorphine trial. Human Psychopharmacology: Clinical and Experimental 2013;28(5):506-515.

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