ArticleStatistical methods in medical research2020
Criteria for evaluating risk prediction of multiple outcomes.
Article in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A decision-analytical perspective on incorporating multiple outcomes in the production of clinical prediction models: defining a taxonomy of risk estimands.BMC medicine · 2025Article
- A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events.Clinical epigenetics · 2022Article
- Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification.Nature genetics · 2022Article
- Predicting correlated outcomes from molecular data.Bioinformatics (Oxford, England) · 2021Article
- Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation.Diagnostic and prognostic research · 2021Article
- EA3: A softmax algorithm for evidence appraisal aggregation.PloS one · 2021Article
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1 author.
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Abstract
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.
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