Simple options for improving signal detection in antidepressant clinical trials.

OBJECTIVE

Previous experience with antidepressant studies highlight the difficulties in discriminating an effective drug from placebo. In hopes of improving signal detection, three easy-to-implement methodologies were employed during the development of a recently approved antidepressant.

EXPERIMENTAL DESIGN

Results from alternative and traditional methods could be compared directly because most studies employed both methods. This database included 11 double-blind, placebo-controlled trials (some with multiple dose arms and/or active comparators) yielding 22 treatment arms of antidepressants at or above the minimally effective dose noted in their U.S. labels.

PRINCIPAL OBSERVATIONS

Results agreed with the previous evidence showing that the performance of a likelihood-based, mixed-effects model repeated measures (MMRM) analysis was superior to that of analysis of covariance with missing values imputed using the last observation carried forward (LOCF) approach; MMRM correctly identified drug as superior to placebo in 14/22 (63.6%) comparisons versus 11/22 (50.0%) for LOCF. In agreement with previous studies, use of subscales of the Hamilton Depression Rating scale (HAMD) improved signal detection compared to the HAMD total score. Using MMRM with HAMD subscales correctly identified drug as superior to placebo in up to 17/22 (77.3%) comparisons. Excluding double-blind, placebo lead-in responders did not increase the frequency of correctly identifying drug-versus-placebo differences.

CONCLUSIONS

The 22 drug-versus-placebo comparisons in this report offer a small amount of evidence and therefore may not be convincing on their own, although results do agree with previous research. Researchers may be able to take advantage of these easy-to-implement methods while we wait for further improvements in other areas.