Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis.

Seo, Michael; White, Ian R; Furukawa, Toshi A; Imai, Hissei; Valgimigli, Marco; Egger, Matthias; Zwahlen, Marcel; Efthimiou, Orestis (2021). Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis. Statistics in medicine, 40(6), pp. 1553-1573. Wiley-Blackwell 10.1002/sim.8859

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Meta-analysis of individual patient data (IPD) is increasingly used to synthesize data from multiple trials. IPD meta-analysis offers several advantages over meta-analyzing aggregate data, including the capacity to individualize treatment recommendations. Trials usually collect information on many patient characteristics. Some of these covariates may strongly interact with treatment (and thus be associated with treatment effect modification) while others may have little effect. It is currently unclear whether a systematic approach to the selection of treatment-covariate interactions in an IPD meta-analysis can lead to better estimates of patient-specific treatment effects. We aimed to answer this question by comparing in simulations the standard approach to IPD meta-analysis (no variable selection, all treatment-covariate interactions included in the model) with six alternative methods: stepwise regression, and five regression methods that perform shrinkage on treatment-covariate interactions, that is, least absolute shrinkage and selection operator (LASSO), ridge, adaptive LASSO, Bayesian LASSO, and stochastic search variable selection. Exploring a range of scenarios, we found that shrinkage methods performed well for both continuous and dichotomous outcomes, for a variety of settings. In most scenarios, these methods gave lower mean squared error of the patient-specific treatment effect as compared with the standard approach and stepwise regression. We illustrate the application of these methods in two datasets from cardiology and psychiatry. We recommend that future IPD meta-analysis that aim to estimate patient-specific treatment effects using multiple effect modifiers should use shrinkage methods, whereas stepwise regression should be avoided.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Cardiovascular Disorders (DHGE) > Clinic of Cardiology
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; Valgimigli, Marco; Egger, Matthias; Zwahlen, Marcel and Efthimiou, Orestis

Subjects:

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

ISSN:

0277-6715

Publisher:

Wiley-Blackwell

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Doris Kopp Heim

Date Deposited:

04 Jan 2021 16:42

Last Modified:

13 Mar 2021 20:00

Publisher DOI:

10.1002/sim.8859

PubMed ID:

33368415

Uncontrolled Keywords:

Bayesian analysis individual patient data meta-regression shrinkage variable selection

BORIS DOI:

10.48350/150602

URI:

https://boris.unibe.ch/id/eprint/150602

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