The association of dropout and outcome in trials of antipsychotic medication and its implications for dealing with missing data

Abstract

Objective: The extent to which noncompletion of a clinical trial relates to outcomes has implications for choosing the most appropriate method for contending with missing data due to dropout. We examined whether dropout relates to outcome in clinical trials of antipsychotic medication.

Methods: Data from 5 large clinical trials of schizophrenia (n=3483) were examined separately. Patients were aggregated into groups based on their final study visit. Group mean Positive and Negative Syndrome Scale (PANSS) total scores for each visit were computed and graphed. Change from baseline to end point for each group was computed and examined using ANCOVA. Cox regression modeling was used to examine baseline PANSS total and change as predictors of time to dropout.

Results: In all 5 trials there was a statistically significantly relationship between time in trial and improvement. The longer the patients remained in the trial the more that they improved, with trial completers showing the most improvement at each time point. Higher baseline PANSS scores and symptom deterioration indicated by increased PANSS preceding the final study visit prior to dropout corresponded significantly with a greater likelihood of dropout.

Conclusions: Dropout in clinical trials of antipsychotic medications corresponds with efficacy outcomes, the dynamics of symptom change and baseline symptom severity. Therefore, methods for statistical analysis should examine both efficacy and dropout and cannot assume that missing data due to dropout are completely at random.

Fig. 1.

Mean Positive and Negative Syndrome Scale Total Score by Study Week Grouped by Final Visit Attended for 5 Clinical Trials.