Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data

Autor: Lorenzo Lorenzo-Luaces, Allison Peipert, Natalie Rodriguez-Quintana, Robinson De Jesús Romero, Lauren A. Rutter
Rok vydání: 2020
Předmět:
Zdroj: International Journal of Cognitive Therapy. 14:59-85
ISSN: 1937-1217
DOI: 10.1007/s41811-020-00094-3
Popis: Cognitive-behavioral therapies (CBTs) are the most widely studied form of psychotherapy for disorders like depression and anxiety. Nonetheless, there is heterogeneity in response to CBTs vs. other treatments. Researchers have become increasingly interested in using pre-treatment individual differences (i.e., moderators) to match patients to the most effective treatments for them. Several methods to combine multiple variables to create precision treatment rules (PTRs) that identify subgroups have been proposed. We review the rationale behind multivariable PTRs as well as the findings of studies that have used different PTRs. We identify conceptual and methodological issues in the literature. Multivariable treatment assignment is a promising avenue of research. Nonetheless, effect sizes appear to be small and most of the samples that have been used to study these questions have been grossly underpowered to detect small effects. We recommend researchers explore multivariable treatment selection strategies, particularly those resembling risk stratification, in heterogeneous samples of patients undergoing low-intensity CBTs vs. realistic minimal controls.
Databáze: OpenAIRE
Externí odkaz: