On the fallibility of placebo control and how to address it: a case study in psychedelic microdosing

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In medical research, the gold standard experimental design is the blinded randomized controlled trial. Despite the central role of blinding, it is rare for trials to assess blinding integrity and to incorporate this information into the interpretation of results. Here we use computational modelling to show that the combination of weak blinding and positive treatment expectancy can lead to activated expectancy bias (AEB), which is an uneven distribution of expectancy effects between the treatment arms due to patients recognizing their treatment allocation. We show that this bias can inflate estimates of treatment effects and potentially create false positive findings. To counteract this bias, we introduce the Correct Guess Rate Curve (CGRC), a novel analytical tool that can estimate what would be the outcome of a perfectly blinded trial based on data from an imperfectly blinded trial. We apply CGRC to pseudo-experimental data generated by our computational model and show that the method produces AEB corrected results. Furthermore, to demonstrate the impact of AEB and the utility of the CGRC on empirical data, we re-analyzed data from a previously published self-blinding microdose trial. Results suggest that the observed placebo vs. microdose differences are susceptible to AEB, therefore, at risk of being false positives. These results demonstrate that a placebo control group is in itself not sufficient to control for expectancy effects, arguing that placebo-controlled studies are more fallible than conventionally assumed, which has implications for evidence-based medicine and numerous public health policies.

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