Martin, GP, Sperrin, M, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Buchan, I and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2020) Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches. Statistics in Medicine.

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Abstract

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction.

Item Type: Article
Additional Information: © 2020 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: binary outcomes; clinical prediction model; multiple outcomes; multivariate modeling; regression; risk prediction
Subjects: R Medicine > R Medicine (General)
R Medicine > R Medicine (General) > R735 Medical education. Medical schools. Research
R Medicine > RA Public aspects of medicine
Divisions: Faculty of Medicine and Health Sciences > School of Primary, Community and Social Care
Related URLs:
Depositing User: Symplectic
Date Deposited: 13 Nov 2020 11:15
Last Modified: 13 Nov 2020 11:15
URI: https://eprints.keele.ac.uk/id/eprint/8883

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