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Criteria for evaluating risk prediction of multiple outcomes

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journal contribution
posted on 2020-07-20, 15:09 authored by Frank Dudbridge
Risk prediction models have been developed in many contexts to classify individuals according to a single outcome, such as risk of a disease. Emerging “-omic” biomarkers provide panels of features that can simultaneously predict multiple outcomes from a single biological sample, creating issues of multiplicity reminiscent of exploratory hypothesis testing. Here I propose definitions of some basic criteria for evaluating prediction models of multiple outcomes. I define calibration in the multivariate setting and then distinguish between outcome-wise and individual-wise prediction, and within the latter between joint and panel-wise prediction. I give examples such as screening and early detection in which different senses of prediction may be more appropriate. In each case I propose definitions of sensitivity, specificity, concordance, positive and negative predictive value and relative utility. I link the definitions through a multivariate probit model, showing that the accuracy of a multivariate prediction model can be summarised by its covariance with a liability vector. I illustrate the concepts on a biomarker panel for early detection of eight cancers, and on polygenic risk scores for six common diseases.

History

Citation

Statistical Methods in Medical Research, 2020, https://doi.org/10.1177/0962280220929039

Author affiliation

Department of Health Sciences

Version

  • AM (Accepted Manuscript)

Published in

Statistical Methods in Medical Research

Pagination

962280220929039

Publisher

SAGE Publications

issn

0962-2802

eissn

1477-0334

Copyright date

2020

Available date

2020-06-29

Spatial coverage

England

Language

eng

Publisher version

https://journals.sagepub.com/doi/full/10.1177/0962280220929039

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