Covariate adjustment can potentially improve efficiency in randomized clinical trials, but in rare diseases, limited prior knowledge makes it difficult to specify an adjustment at the design stage. Targeted maximum likelihood estimation (TMLE) with the Super Learner (TMLE+SL) can outperform fixed adjustment methods, such as the G-computation estimator, by leveraging machine learning to learn both the covariates and the functional form of the adjustment from the trial data. Estimating the known treatment mechanism can improve efficiency further. In simulations reflecting real rare-disease trials, TMLE+SL yielded valid inference and achieved greater efficiency and power than conventional analyses. Finally, we also provide practical guidance on specifying TMLE+SL in rare-disease trials.

翻译：暂无翻译