Background: Clinical trials testing the effectiveness of interventions for addictions, HIV transmission risk, and other behavioral health problems are important to advancing evidence-based treatment. Such trials are expensive and time-consuming to conduct, but the underlying effect sizes tend to be modest, and often findings are disappointing, failing to show evidence of treatment effects.
Objectives: To demonstrate how appropriate covariation for baseline severity can enhance detection of treatment effects.
Methods: Explication and case example.
Results: Baseline severity (the score of the outcome measure at baseline, prior to randomization) is often strongly associated with outcome in such studies. Covariation for baseline score may enhance detection of treatment effects, because the variance explained by the baseline score is removed from the error variance in the estimate of the difference in outcome between treatments. Alternatively, the effect of treatment may manifest in the form of a baseline-by-treatment interaction. Common interaction patterns include that treatment may be more effective among patients with higher levels of baseline severity, or treatment may be more effective among patients with low severity at baseline ('relapse prevention' effect). Such effects may be important to developing treatment guidelines and offer clues toward understanding the mechanisms of action of treatments and of the disorders.
Conclusions and scientific significance: This article illustrates principles of covariation for baseline and the baseline-by-treatment interaction in nontechnical graphical terms, and discusses examples from clinical trials. Implications for the design and analysis of clinical trials are discussed, and it is argued that covariation for baseline severity of the outcome measure and testing of the baseline-by-treatment interaction should be considered for inclusion in the primary outcome analyses of treatment effectiveness trials of substantial size.