Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example

Wester RA, Rubel J, Mayer A (2022)
Clinical Psychological Science 10(5): 920-940.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
OA 1.57 MB
Autor*in
Wester, Robin Anno; Rubel, Julian; Mayer, AxelUniBi
Abstract / Bemerkung
Estimating individual treatment effects (ITEs) is crucial to personalized psychotherapy. It depends on identifying all covariates that interact with treatment, a challenging task considering the many patient characteristics hypothesized to influence treatment outcome. The goal of this study was to compare different covariate-selection strategies and their consequences on estimating ITEs. A Monte Carlo simulation was conducted to compare stepwise regression with and without cross-validation and shrinkage methods. The study was designed to mimic the setting of psychotherapy studies. No single covariate-selection strategy dominated all others across all factor-level combinations and on all performance measures. The least absolute shrinkage and selection operator showed the most accurate out-of-sample predictions, identified the highest number of true treatment-covariate interactions, and estimated ITEs with the highest precision across the most conditions. Domain backward stepwise regression and backward stepwise regression using Bayesian information criterion were least biased in estimating variance of ITEs across the most conditions.
Stichworte
personalized medicine; individual treatment effects; machine learning; lasso; stepwise regression; depression
Erscheinungsjahr
2022
Zeitschriftentitel
Clinical Psychological Science
Band
10
Ausgabe
5
Seite(n)
920-940
ISSN
2167-7026
eISSN
2167-7034
Page URI
https://pub.uni-bielefeld.de/record/2955567

Zitieren

Wester RA, Rubel J, Mayer A. Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example. Clinical Psychological Science . 2022;10(5):920-940.
Wester, R. A., Rubel, J., & Mayer, A. (2022). Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example. Clinical Psychological Science , 10(5), 920-940. https://doi.org/10.1177/21677026211071043
Wester, Robin Anno, Rubel, Julian, and Mayer, Axel. 2022. “Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example”. Clinical Psychological Science 10 (5): 920-940.
Wester, R. A., Rubel, J., and Mayer, A. (2022). Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example. Clinical Psychological Science 10, 920-940.
Wester, R.A., Rubel, J., & Mayer, A., 2022. Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example. Clinical Psychological Science , 10(5), p 920-940.
R.A. Wester, J. Rubel, and A. Mayer, “Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example”, Clinical Psychological Science , vol. 10, 2022, pp. 920-940.
Wester, R.A., Rubel, J., Mayer, A.: Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example. Clinical Psychological Science . 10, 920-940 (2022).
Wester, Robin Anno, Rubel, Julian, and Mayer, Axel. “Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example”. Clinical Psychological Science 10.5 (2022): 920-940.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Creative Commons Namensnennung 4.0 International Public License (CC-BY 4.0):
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2022-10-21T15:00:09Z
MD5 Prüfsumme
2d242bee93820d06bdae2a771acdb0a7

Link(s) zu Volltext(en)
Access Level
OA Open Access
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

Preprint: 10.31234/osf.io/t5843

Suchen in

Google Scholar