A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials

摘要

ObjectivesDespite efforts to identify characteristics associated with medication-placebo differences in antidepressant trials, few consistent findings have emerged to guide participant selection in drug development settings and differential therapeutics in clinical practice. Limitations in the methodologies used, particularly searching for a single moderator while treating all other variables as noise, may partially explain the failure to generate consistent results. The present study tested whether interactions between pre-treatment patient characteristics, rather than a single-variable solution, may better predict who is most likely to benefit from placebo vs. medication.