Combining efficacy and completion rates with no data imputation: A composite approach with greater sensitivity for the statistical evaluation of active comparisons in antipsychotic trials

Outcomes in RCT's of antipsychotic medications are often examined using last observation carried forward (LOCF) and mixed effect models (MMRM), these ignore meaning of non-completion and thus rely on questionable assumptions. We tested an approach that combines into a single statistic, the drug effect in those who complete trial and proportion of patients in each treatment group who complete trial. This approach offers a conceptually and clinically meaningful endpoint. Composite approach was compared to LOCF (ANCOVA) and MMRM in 59 industry sponsored RCT's. For within study comparisons we computed effect size (z-score) and p values for (a) rates of completion, (b) symptom change for complete cases, which were combined into composite statistic, and (c) symptom change for all cases using last observation forward (LOCF). In the 30 active comparator studies, composite approach detected larger differences in effect size than LOCF (ES=.05) and MMRM (ES=.076). In 10 of the 49 comparisons composite lead to significant differences (p ≤ .05) where LOCF and MMRM did not. In 3 comparisons LOCF was significant, in 2 MMRM lead to significant differences whereas composite did not. In placebo controlled trials, there was no meaningful difference in effect size between composite and LOCF and MMRM when comparing placebo to active treatment, however composite detected greater differences than other approaches when comparing between active treatments. Composite was more sensitive to effects of experimental treatment vs. active controls (but not placebo) than LOCF and MMRM thereby increasing study power while answering a more relevant question.