Abstract
Attrition from clinical trials is unavoidable in geriatric psychiatry and beyond. It results in incomplete data and consequently imposes three fundamental challenges: greater bias, reduced power, and less generalizability. In an effort to assess the extent of attrition and the relevance of statistical methods applied to analyze incomplete data in geriatric psychiatry, we reviewed 69 published antidepressant randomized clinical trials conducted since 1975. The median attrition rate estimated from these trials was 26.6%; nevertheless, we found that many trials lack data analytic strategies to address the problem of attrition. Most of the applied statistical analyses involved chi-square tests, t-tests, and analysis of variance (ANOVA), each of which assume that data are missing completely at random. Even when imputation for missing data due to attrition was attempted, only the last observation carried forward (LOCF) method was implemented. The LOCF imputation can actually increase bias of the results in the analysis of repeatedly measured outcomes. In addition, despite the longitudinal nature of repeatedly measured outcomes, the statistical methods used are for analysis of cross-sectional data. Thus, the data analytic strategies did not adequately meet the challenges arising from attrition. We encourage the use of mixed-effects models to reduce the impact of attrition on bias, power and generalizability in antidepressant RCTs for geriatric depression. For imputation, we recommend use of multiple imputation methods instead of LOCF.
Keywords: last observation carried forward method, intent-to-treat sample, Montgomery-Asberg Depression Rating Scale, ANOVA, Hamilton Rating Scale of Depression
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