Developing prediction models when there are systematically missing predictors in individual patient data meta-analysis.

Seo, Michael; Furukawa, Toshi A; Karyotaki, Eirini; Efthimiou, Orestis (2023). Developing prediction models when there are systematically missing predictors in individual patient data meta-analysis. Research Synthesis Methods, 14(3), pp. 455-467. Wiley 10.1002/jrsm.1625

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Clinical prediction models are widely used in modern clinical practice. Such models are often developed using individual patient data (IPD) from a single study, but often there are IPD available from multiple studies. This allows using meta-analytical methods for developing prediction models, increasing power and precision. Different studies, however, often measure different sets of predictors, which may result to systematically missing predictors, i.e. when not all studies collect all predictors of interest. This situation poses challenges in model development. We hereby describe various approaches that can be used to develop prediction models for continuous outcomes in such situations. We compare four approaches: a "restrict predictors" approach, where the model is developed using only predictors measured in all studies; a multiple imputation approach that ignores study-level clustering; a multiple imputation approach that accounts for study-level clustering; and a new approach that develops a prediction model in each study separately using all predictors reported, and then synthesizes all predictions in a multi-study ensemble. We explore in simulations the performance of all approaches under various scenarios. We find that imputation methods and our new method outperform the restrict predictors approach. In several scenarios, our method outperformed imputation methods, especially for few studies, when predictor effects were small, and in case of large heterogeneity. We use a real dataset of 12 trials in psychotherapies for depression to illustrate all methods in practice, and we provide code in R. This article is protected by copyright. All rights reserved.

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, Efthimiou, Orestis

Subjects:

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

ISSN:

1759-2879

Publisher:

Wiley

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Pubmed Import

Date Deposited:

10 Feb 2023 08:36

Last Modified:

10 Feb 2024 00:25

Publisher DOI:

10.1002/jrsm.1625

PubMed ID:

36755407

Uncontrolled Keywords:

ensemble predictive modelling individual patient data meta-analysis multilevel model prediction research

BORIS DOI:

10.48350/178600

URI:

https://boris.unibe.ch/id/eprint/178600

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