Brief

Researchers have developed a new R package called `crsurvlearners` to help estimate conditional average treatment effects (CATEs) in competing risks settings. This is particularly useful in personalized medicine where understanding treatment effectiveness for a specific event, while accounting for other potential events, is crucial. The package systematically compares six meta-learners that combine different risk modeling and direct CATE modeling approaches, evaluating their performance across various simulation settings to guide model selection. AI