Statistical choices can affect inferences about treatment efficacy: a case study from obsessive-compulsive disorder research.

Longitudinal clinical trials in psychiatry have used various statistical methods to examine treatment effects. The validity of the inferences depends upon the different method's assumptions and whether a given study violates those assumptions. The objective of this paper was to elucidate these complex issues by comparing various methods for handling missing data (e.g., last observation carried forward [LOCF], completer analysis, propensity-adjusted multiple imputation) and for analyzing outcome (e.g., end-point analysis, repeated-measures analysis of variance [RM-ANOVA], mixed-effects models [MEMs]) using data from a multi-site randomized controlled trial in obsessive-compulsive disorder (OCD). The trial compared the effects of 12 weeks of exposure and ritual prevention (EX/RP), clomipramine (CMI), their combination (EX/RP&CMI) or pill placebo in 122 adults with OCD. The primary outcome measure was the Yale-Brown Obsessive Compulsive Scale. For most comparisons, inferences about the relative efficacy of the different treatments were impervious to different methods for handling missing data and analyzing outcome. However, when EX/RP was compared to CMI and when CMI was compared to placebo, traditional methods (e.g., LOCF, RM-ANOVA) led to different inferences than currently recommended alternatives (e.g., multiple imputation based on estimation-maximization algorithm, MEMs). Thus, inferences about treatment efficacy can be affected by statistical choices. This is most likely when there are small but potentially clinically meaningful treatment differences and when sample sizes are modest. The use of appropriate statistical methods in psychiatric trials can advance public health by ensuring that valid inferences are made about treatment efficacy.