Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis.
<h4>Background</h4>Various treatments are recommended as first-line options in practice guidelines for depression, but it is unclear which is most efficacious for a given person. Accurate individualized predictions of relative treatment effects are needed to optimize treatment recommenda...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0322124 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849315993697910784 |
|---|---|
| author | Ellen Driessen Orestis Efthimiou Frederik J Wienicke Jasmijn Breunese Pim Cuijpers Thomas P A Debray David J Fisher Marjolein Fokkema Toshiaki A Furukawa Steven D Hollon Anuj H P Mehta Richard D Riley Madison R Schmidt Jos W R Twisk Zachary D Cohen |
| author_facet | Ellen Driessen Orestis Efthimiou Frederik J Wienicke Jasmijn Breunese Pim Cuijpers Thomas P A Debray David J Fisher Marjolein Fokkema Toshiaki A Furukawa Steven D Hollon Anuj H P Mehta Richard D Riley Madison R Schmidt Jos W R Twisk Zachary D Cohen |
| author_sort | Ellen Driessen |
| collection | DOAJ |
| description | <h4>Background</h4>Various treatments are recommended as first-line options in practice guidelines for depression, but it is unclear which is most efficacious for a given person. Accurate individualized predictions of relative treatment effects are needed to optimize treatment recommendations for depression and reduce this disorder's vast personal and societal costs.<h4>Aims</h4>We describe the protocol for a systematic review and individual participant data (IPD) network meta-analysis (NMA) to inform personalized treatment selection among five major empirically-supported depression treatments.<h4>Method</h4>We will use the METASPY database to identify randomized clinical trials that compare two or more of five treatments for adult depression: antidepressant medication, cognitive therapy, behavioral activation, interpersonal psychotherapy, and psychodynamic therapy. We will request IPD from identified studies. We will conduct an IPD-NMA and develop a multivariable prediction model that estimates individualized relative treatment effects from demographic, clinical, and psychological participant characteristics. Depressive symptom level at treatment completion will constitute the primary outcome. We will evaluate this model using a range of measures for discrimination and calibration, and examine its potential generalizability using internal-external cross-validation.<h4>Conclusions</h4>We describe a state-of-the-art method to predict personalized treatment effects based on IPD from multiple trials. The resulting prediction model will need prospective evaluation in mental health care for its potential to inform shared decision-making. This study will result in a unique database of IPD from randomized clinical trials around the world covering five widely used depression treatments, available for future research. |
| format | Article |
| id | doaj-art-7e2e815a943e498bb74c58f2d026008d |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-7e2e815a943e498bb74c58f2d026008d2025-08-20T03:51:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01204e032212410.1371/journal.pone.0322124Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis.Ellen DriessenOrestis EfthimiouFrederik J WienickeJasmijn BreunesePim CuijpersThomas P A DebrayDavid J FisherMarjolein FokkemaToshiaki A FurukawaSteven D HollonAnuj H P MehtaRichard D RileyMadison R SchmidtJos W R TwiskZachary D Cohen<h4>Background</h4>Various treatments are recommended as first-line options in practice guidelines for depression, but it is unclear which is most efficacious for a given person. Accurate individualized predictions of relative treatment effects are needed to optimize treatment recommendations for depression and reduce this disorder's vast personal and societal costs.<h4>Aims</h4>We describe the protocol for a systematic review and individual participant data (IPD) network meta-analysis (NMA) to inform personalized treatment selection among five major empirically-supported depression treatments.<h4>Method</h4>We will use the METASPY database to identify randomized clinical trials that compare two or more of five treatments for adult depression: antidepressant medication, cognitive therapy, behavioral activation, interpersonal psychotherapy, and psychodynamic therapy. We will request IPD from identified studies. We will conduct an IPD-NMA and develop a multivariable prediction model that estimates individualized relative treatment effects from demographic, clinical, and psychological participant characteristics. Depressive symptom level at treatment completion will constitute the primary outcome. We will evaluate this model using a range of measures for discrimination and calibration, and examine its potential generalizability using internal-external cross-validation.<h4>Conclusions</h4>We describe a state-of-the-art method to predict personalized treatment effects based on IPD from multiple trials. The resulting prediction model will need prospective evaluation in mental health care for its potential to inform shared decision-making. This study will result in a unique database of IPD from randomized clinical trials around the world covering five widely used depression treatments, available for future research.https://doi.org/10.1371/journal.pone.0322124 |
| spellingShingle | Ellen Driessen Orestis Efthimiou Frederik J Wienicke Jasmijn Breunese Pim Cuijpers Thomas P A Debray David J Fisher Marjolein Fokkema Toshiaki A Furukawa Steven D Hollon Anuj H P Mehta Richard D Riley Madison R Schmidt Jos W R Twisk Zachary D Cohen Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis. PLoS ONE |
| title | Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis. |
| title_full | Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis. |
| title_fullStr | Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis. |
| title_full_unstemmed | Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis. |
| title_short | Developing a multivariable prediction model to support personalized selection among five major empirically-supported treatments for adult depression. Study protocol of a systematic review and individual participant data network meta-analysis. |
| title_sort | developing a multivariable prediction model to support personalized selection among five major empirically supported treatments for adult depression study protocol of a systematic review and individual participant data network meta analysis |
| url | https://doi.org/10.1371/journal.pone.0322124 |
| work_keys_str_mv | AT ellendriessen developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT orestisefthimiou developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT frederikjwienicke developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT jasmijnbreunese developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT pimcuijpers developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT thomaspadebray developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT davidjfisher developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT marjoleinfokkema developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT toshiakiafurukawa developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT stevendhollon developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT anujhpmehta developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT richarddriley developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT madisonrschmidt developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT joswrtwisk developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis AT zacharydcohen developingamultivariablepredictionmodeltosupportpersonalizedselectionamongfivemajorempiricallysupportedtreatmentsforadultdepressionstudyprotocolofasystematicreviewandindividualparticipantdatanetworkmetaanalysis |