Subgroup Treatment Effect Estimation in Clinical Trials
Description
Naive and adjusted treatment effect estimation for subgroups. Model
averaging (Bornkamp et.al, 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. For details, see Ballarini et.al. (2021) .