{ "_id": "6a115445acfb0bcc41ceacf7", "Package": "forestsearch", "Title": "Exploratory Subgroup Identification in Clinical Trials with\nSurvival Endpoints", "Version": "0.1.0", "Authors@R": "person(\"Larry\", \"Leon\", , \"larry.leon.05@post.harvard.edu\", role = c(\"aut\", \"cre\"))", "Description": "Implements statistical methods for exploratory subgroup\nidentification in clinical trials with survival endpoints.\nProvides tools for identifying patient subgroups with\ndifferential treatment effects using machine learning\napproaches including Generalized Random Forests (GRF), LASSO\nregularization, and exhaustive combinatorial search algorithms.\nFeatures bootstrap bias correction using infinitesimal\njackknife methods to address selection bias in post-hoc\nanalyses. Designed for clinical researchers conducting\nexploratory subgroup analyses in randomized controlled trials,\nparticularly for multi-regional clinical trials (MRCT)\nrequiring regional consistency evaluation. 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