A decision-analytical perspective on incorporating multiple outcomes in the production of clinical prediction models: defining a taxonomy of risk estimands

Abstract Background Clinical prediction models (CPMs) estimate an individual’s risk of current or future outcome events, using information available about the individual at the time of prediction. While most CPMs are developed to predict a single outcome event, many clinical decisions require considering the risks of multiple outcome events. For example, decision-making for anticoagulation therapy involves assessing an individual’s risks of both blood clot and bleeding, while decision-making around interventions for multimorbidity prevention requires an understanding of the risks of developing multiple long-term conditions. However, determining when and how to incorporate multiple outcomes into CPMs remains challenging. This article aims to raise awareness of multiple outcome prediction and present clinical examples where such prediction is essential to help inform individual decision-making. Main text A range of analytical methods are available to develop multiple-outcome CPMs, but there are frequent malapropisms and heterogeneity in terminology across this literature, making it difficult to identify/compare possible methods. Selecting the appropriate method should depend on the intended risk estimand—the type of predicted risks that we wish the CPM to estimate—but this is often not defined or reported. Using clinical examples and a decision-analytical perspective, we present a taxonomy of risk estimands to frame different clinical contexts requiring multiple-outcome CPMs. We outline four levels of risk estimands: (i) single-outcome risk, (ii) competing-outcome risk, (iii) composite-outcome risk, and (iv) risk of multiple outcome combinations. We demonstrate how a decision-analytical and utility-theory lens can help define the risk estimand for a given clinical scenario, based on the model’s intended use. Conclusions Clearly defining and reporting the risk estimand is essential for all prediction model studies. A decision-analytical framework aids in selecting the most appropriate estimand for a given prediction task and in determining when and how to incorporate multiple outcomes into CPM development.