LAUSR

Key Points Question How can machine learning be used to estimate per-protocol effects in randomized clinical trials? Findings In a cohort of 1227 women derived from secondary analysis of a randomized clinical trial, ensemble machine learning with augmented inverse probability weighting was used to estimate the per-protocol effect of daily low-dose aspirin on pregnancy detected using human chorionic gonadotropin (hCG) levels. Relative to placebo, adherence to the assigned treatment protocol was associated with an increase of 8.0 hCG-detected pregnancies per 100 women, approximately double the intention-to-treat estimates. Meaning These findings suggest that in per-protocol analysis, machine learning techniques may allow for confounder adjustment while reducing the occurrence of model misspecification.