Turn on, Tune in, and Drop out: Predictors of Attrition in a Prospective Observational Cohort Study on Psychedelic Use.

backgroundThe resurgence of research and public interest in the positive psychological effects of psychedelics, together with advancements in digital data collection techniques, have brought forth a new type of research design, which involves prospectively gathering large-scale naturalistic data from psychedelic users; that is, before and after the use of a psychedelic compound. A methodological limitation of such studies is their high attrition rate, particularly owing to participants who stop responding after initial study enrollment. Importantly, study dropout can introduce systematic biases that may affect the interpretability of results.

objectiveBased on a previously collected sample (baseline n=654), here we investigated potential determinants of study attrition in web-based prospective studies on psychedelic use.

methodsLogistic regression models were used to examine demographic, psychological trait and state, and psychedelic-specific predictors of dropout. Predictors were assessed 1 week before, 1 day after, and 2 weeks after psychedelic use, with attrition being defined as noncompletion of the key endpoint 4 weeks post experience.

resultsPredictors of attrition were found among demographic variables including age (β=0.024; P=.007) and educational levels, as well as personality traits, specifically conscientiousness (β=-0.079; P=.02) and extraversion (β=0.082; P=.01). Contrary to prior hypotheses, neither baseline attitudes toward psychedelics nor the intensity of acute challenging experiences were predictive of dropout.

conclusionsThe baseline predictors of attrition identified here are consistent with those reported in longitudinal studies in other scientific disciplines, suggesting their transdisciplinary relevance. Moreover, the lack of an association between attrition and psychedelic advocacy or negative drug experiences in our sample contextualizes concerns about problematic biases in these and related data.