Can personalized treatment prediction improve the outcomes, compared with the group average approach, in a randomized trial? Developing and validating a multivariable prediction model in a pragmatic megatrial of acute treatment for major depression.

Furukawa, Toshi A; Debray, Thomas P A; Akechi, Tatsuo; Yamada, Mitsuhiko; Kato, Tadashi; Seo, Michael; Efthimiou, Orestis (2020). Can personalized treatment prediction improve the outcomes, compared with the group average approach, in a randomized trial? Developing and validating a multivariable prediction model in a pragmatic megatrial of acute treatment for major depression. Journal of Affective Disorders, 274, pp. 690-697. Elsevier 10.1016/j.jad.2020.05.141

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BACKGROUND

Clinical trials have traditionally been analysed at the aggregate level, assuming that the group average would be applicable to all eligible and similar patients. We re-analyzed a mega-trial of antidepressant therapy for major depression to explore whether a multivariable prediction model may lead to different treatment recommendations for individual participants.

METHODS

The trial compared the second-line treatment strategies of continuing sertraline, combining it with mirtazapine or switching to mirtazapine after initial failure to remit on sertraline among 1,544 patients with major depression. The outcome was the Personal Health Questionnaire-9 (PHQ-9) at week 9: the original analyses showed that both combining and switching resulted in greater reduction in PHQ-9 by 1.0 point than continuing. We considered several models of penalized regression or machine learning.

RESULTS

Models using support vector machines (SVMs) provided the best performance. Using SVMs, continuing sertraline was predicted to be the best treatment for 123 patients, combining for 696 patients, and switching for 725 patients. In the last two subgroups, both combining and switching were equally superior to continuing by 1.2 to 1.4 points, resulting in the same treatment recommendations as with the original aggregate data level analyses; in the first subgroup, however, switching was substantively inferior to combining (-3.1, 95%CI: -5.4 to -0.5).

LIMITATIONS

Stronger predictors are needed to make more precise predictions.

CONCLUSIONS

The multivariable prediction models led to improved recommendations for a minority of participants than the group average approach in a megatrial.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

Graduate School:

Graduate School for Health Sciences (GHS)

UniBE Contributor:

Seo, Michael Juhn Uh and Efthimiou, Orestis

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

0165-0327

Publisher:

Elsevier

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Andrea Flükiger-Flückiger

Date Deposited:

22 Jul 2020 20:18

Last Modified:

21 Oct 2020 17:03

Publisher DOI:

10.1016/j.jad.2020.05.141

PubMed ID:

32664003

Uncontrolled Keywords:

Antidepressive agents Depressive disorder Precision medicine Prediction model

BORIS DOI:

10.7892/boris.145295

URI:

https://boris.unibe.ch/id/eprint/145295

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