Subgroup Treatment Effect Estimation in Clinical Trials

Naive and adjusted treatment effect estimation for subgroups. Model averaging (Bornkamp, 2016 ) and bagging (Rosenkranz, 2016 ) are proposed to address the problem of selection bias in treatment effect estimates for subgroups. The package can be used for all commonly encountered type of outcomes in clinical trials (continuous, binary, survival, count). Additional functions are provided to build the subgroup variables to be used and to plot the results using forest plots.



Version 0.3-5 (March 2019)

  • Implemented a confint function to obtain confidence intervals in a separate function without re-running the models.
  • Solves a problem that was caused by small sample sizes in subgroups in bootstrap samples

Version 0.3-4 (January 2019)

  • Updated the description and vignette titles.

Version 0.3-3 (December 2018)

  • First release.

Reference manual

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0.3-5 by Nicolas Ballarini, a year ago

Browse source code at

Authors: Nicolas Ballarini [aut, cre] , Bjoern Bornkamp [aut] , Marius Thomas [aut, cre] , Baldur Magnusson [ctb]

Documentation:   PDF Manual  

GPL-2 license

Imports MASS, ggplot2, survival, matrixStats

Suggests knitr, parallel

See at CRAN