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Exploratory Subgroup Identification in Clinical Trials with Survival Endpoints

v0.1.0 · Mar 23, 2026 · MIT + file LICENSE

Description

Implements statistical methods for exploratory subgroup identification in clinical trials with survival endpoints. Provides tools for identifying patient subgroups with differential treatment effects using machine learning approaches including Generalized Random Forests (GRF), LASSO regularization, and exhaustive combinatorial search algorithms. Features bootstrap bias correction using infinitesimal jackknife methods to address selection bias in post-hoc analyses. Designed for clinical researchers conducting exploratory subgroup analyses in randomized controlled trials, particularly for multi-regional clinical trials (MRCT) requiring regional consistency evaluation. Supports both accelerated failure time (AFT) and Cox proportional hazards models with comprehensive diagnostic and visualization tools. Methods are described in León et al. (2024) <doi:10.1002/sim.10163>.

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installed package size

installed size is  6.1Mb
  sub-directories of 1Mb or more:
    doc   4.6Mb
NOTE r-oldrel-windows-x86_64

installed package size

installed size is  6.1Mb
  sub-directories of 1Mb or more:
    doc   4.6Mb

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NOTE 4 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 23, 2026
NOTE r-oldrel-macos-x86_64

installed package size

installed size is  6.1Mb
  sub-directories of 1Mb or more:
    doc   4.6Mb

Dependency Network

Dependencies Reverse dependencies data.table doFuture dplyr foreach future future.apply future.callr ggplot2 glmnet grf gt patchwork policytree progressr randomForest +4 more dependencies forestsearch

Version History

new 0.1.0 Mar 23, 2026