subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials
The investigation of subgroups is an integral part of randomized clinical trials. Exploration of treatment effect heterogeneity is typically performed by covariate-adjusted analyses including treatment-by-covariate interactions. Several statistical techniques, such as model averaging and bagging, we...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2021-10-01
|
| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/3652 |
| _version_ | 1827935823935307776 |
|---|---|
| author | Nicolas M. Ballarini Marius Thomas Gerd K. Rosenkranz Björn Bornkamp |
| author_facet | Nicolas M. Ballarini Marius Thomas Gerd K. Rosenkranz Björn Bornkamp |
| author_sort | Nicolas M. Ballarini |
| collection | DOAJ |
| description | The investigation of subgroups is an integral part of randomized clinical trials. Exploration of treatment effect heterogeneity is typically performed by covariate-adjusted analyses including treatment-by-covariate interactions. Several statistical techniques, such as model averaging and bagging, were proposed recently to address the problem of selection bias in treatment effect estimates for subgroups. In this paper, we describe the subtee R package for subgroup treatment effect estimation. The package can be used for all commonly encountered type of outcomes in clinical trials (continuous, binary, survival, count). We also provide additional functions to build the subgroup variables to be used and to plot the results using forest plots. The functions are demonstrated using data from a clinical trial investigating a treatment for prostate cancer with a survival endpoint. |
| first_indexed | 2024-03-13T08:00:24Z |
| format | Article |
| id | doaj.art-b796e2c66b1b4325b78200df50ba0bf2 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-03-13T08:00:24Z |
| publishDate | 2021-10-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-b796e2c66b1b4325b78200df50ba0bf22023-06-01T18:48:04ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602021-10-019911710.18637/jss.v099.i143493subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical TrialsNicolas M. Ballarini0https://orcid.org/0000-0002-3432-8931Marius Thomas1https://orcid.org/0000-0003-2790-829XGerd K. Rosenkranz2https://orcid.org/0000-0002-8371-2059Björn Bornkamp3https://orcid.org/0000-0002-6294-8185Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, AustriaNovartis Pharma AGSection for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, AustriaNovartis Pharma AGThe investigation of subgroups is an integral part of randomized clinical trials. Exploration of treatment effect heterogeneity is typically performed by covariate-adjusted analyses including treatment-by-covariate interactions. Several statistical techniques, such as model averaging and bagging, were proposed recently to address the problem of selection bias in treatment effect estimates for subgroups. In this paper, we describe the subtee R package for subgroup treatment effect estimation. The package can be used for all commonly encountered type of outcomes in clinical trials (continuous, binary, survival, count). We also provide additional functions to build the subgroup variables to be used and to plot the results using forest plots. The functions are demonstrated using data from a clinical trial investigating a treatment for prostate cancer with a survival endpoint.https://www.jstatsoft.org/index.php/jss/article/view/3652model averagingbootstrapselection biastreatment effect heterogeneity |
| spellingShingle | Nicolas M. Ballarini Marius Thomas Gerd K. Rosenkranz Björn Bornkamp subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials Journal of Statistical Software model averaging bootstrap selection bias treatment effect heterogeneity |
| title | subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials |
| title_full | subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials |
| title_fullStr | subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials |
| title_full_unstemmed | subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials |
| title_short | subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials |
| title_sort | subtee an r package for subgroup treatment effect estimation in clinical trials |
| topic | model averaging bootstrap selection bias treatment effect heterogeneity |
| url | https://www.jstatsoft.org/index.php/jss/article/view/3652 |
| work_keys_str_mv | AT nicolasmballarini subteeanrpackageforsubgrouptreatmenteffectestimationinclinicaltrials AT mariusthomas subteeanrpackageforsubgrouptreatmenteffectestimationinclinicaltrials AT gerdkrosenkranz subteeanrpackageforsubgrouptreatmenteffectestimationinclinicaltrials AT bjornbornkamp subteeanrpackageforsubgrouptreatmenteffectestimationinclinicaltrials |