TY - JOUR
T1 - Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the personalized advantage index approach
AU - Huibers, M.J.H.
AU - Cohen, Z.D.
AU - Lemmens, L.H.J.M.
AU - Arntz, A.
AU - Peeters, F.P.M.L.
AU - Cuijpers, P.
AU - DeRubeis, R.J.
N1 - Funding Information:
Robert J. DeRubeis and Zachary D, Cohen are supported in part by a grant from MQ: Transforming mental health MQ14PM_27. The opinions and assertions contained in this article should not be construed as reflecting the views of the sponsor.
Publisher Copyright:
© 2015 Huibers et al.
PY - 2015
Y1 - 2015
N2 - Introduction Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT) and interpersonal psychotherapy (IPT). Methods 134 depressed patients completed the pre-and post-Treatment BDI-II assessment. First, we identified baseline predictors and moderators. Second, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI), a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach. Results We found five predictors (gender, employment status, anxiety, personality disorder and quality of life) and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events) of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51). Discussion Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although the utility of the PAI approach will need to be evaluated in prospective research, this study promotes the development of a treatment selection approach that can be used in regular mental health care, advancing the goals of personalized medicine.
AB - Introduction Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT) and interpersonal psychotherapy (IPT). Methods 134 depressed patients completed the pre-and post-Treatment BDI-II assessment. First, we identified baseline predictors and moderators. Second, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI), a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach. Results We found five predictors (gender, employment status, anxiety, personality disorder and quality of life) and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events) of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51). Discussion Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although the utility of the PAI approach will need to be evaluated in prospective research, this study promotes the development of a treatment selection approach that can be used in regular mental health care, advancing the goals of personalized medicine.
UR - http://www.scopus.com/inward/record.url?scp=84954139071&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0140771
DO - 10.1371/journal.pone.0140771
M3 - Article
C2 - 26554707
AN - SCOPUS:84954139071
SN - 1932-6203
VL - 10
JO - PLoS One
JF - PLoS One
IS - 11
M1 - e140771
ER -