Longitudinal missing data strategies for substance use clinical trials using generalized estimating equations: an example with a buprenorphine trial

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Abstract

Objective

A review of substance use clinical trials indicates that sub-optimal methods are the most commonly used procedures to deal with longitudinal missing information.

Methods

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.

Results

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 (odds ratios (OR) = 0.57, 95% confidence interval (CI): 0.39–0.83), as did the positive UA imputation model (OR = 0.43, CI: 0.34–0.55). The MI model also demonstrated a similar effect of taper group (OR = 0.57, CI: 0.42–0.77), but the effect size was more similar to that of the listwise deletion model.

Conclusions

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. Copyright © 2013 John Wiley & Sons, Ltd.

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