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subtee: Subgroup Treatment Effect Estimation in Clinical Trials
Naive and adjusted treatment effect estimation for subgroups. Model averaging (Bornkamp et.al, 2016 <doi:10.1002/pst.1796>) and bagging (Rosenkranz, 2016 <doi:10.1002/bimj.201500147>) 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) <doi:10.18637/jss.v099.i14>.
Getting started
Browse package contents
Package details |
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| Author | Nicolas Ballarini [aut, cre], Bjoern Bornkamp [aut], Marius Thomas [aut, cre], Baldur Magnusson [ctb] |
| Maintainer | Nicolas Ballarini <nicoballarini@gmail.com> |
| License | GPL-2 |
| Version | 1.0.1 |
| Package repository | View on CRAN |
| Installation |
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