Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers

Abstract

Background: Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention "works" and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation.

Methods: In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software.

Results: Drawing on Rubin's potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset.

Discussion: Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable "weak spots" of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care.

Keywords: Data analysis; Mental health; Randomized controlled trial; Tutorial.

Fig. 1

Potential and observed outcomes in RCTs. Note: Going from top to bottom, this diagram illustrates the hidden “machinery” inside an RCT. The top panel (A) shows the potential outcomes for each person in our sample if we provide a new treatment (T = 1) or not (T = 0). In our example, “0” means that a person does not suffer from a depressive episode after several weeks, while “1” means that the person still suffers from depression. The potential outcomes are hypothetical; since they are based on counterfactuals, it is impossible to observe both at the same time, and so the true causal effect τ_i of our treatment also remains unobservable. Going down one step, panel B shows the process of randomization, which lets chance decide which potential outcome is realized, and which one is missing (“?”). Loss to follow-up (panel C) adds another layer of missingness. Here, it is much less plausible that the missings are added “completely at random”. As analysts, all we end up having are the observed outcomes at the end of this process, which we need to use to estimate the unobservable causal effect τ on top as closely as possible. Legend: T_i = treatment allocation of patient i (T_i = 0 for no treatment, T_i = 1 for treatment); τ_i = causal treatment effect of patient i; Y_i = outcome of patient i: “1” (red box) if the patient still suffers from depression after several weeks, or “0” (green box) if the patient does not suffer from depression after several weeks; “?” (gray box) if the outcome was not recorded; Y_i^obs = observed outcomes of the trial