Subgroup Treatment Effect Estimation in Clinical Trials
Naive and adjusted treatment effect estimation for subgroups. Model
averaging (Bornkamp et.al, 2016 <10.1002>) and bagging (Rosenkranz, 2016 <10.1002>) 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.10.1002>10.1002>
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)