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.


Reference manual

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0.3-4 by Nicolas Ballarini, a month 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