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>.
Package details |
|
---|---|
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 |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.