Multi-Outcome Risk Prediction Modelling: current state-of-play and future research
Part of a series of three one-hour talks, running over three consecutive Wednesdays. This is the second talk on our short series on modern causal prediction.
Clinical prediction models (CPMs) are used to predict the risk of clinically relevant outcomes or events (e.g. the risk of developing cardiovascular disease within ten years). They are increasingly used to support clinical decisions, yet they seldom reflect the interplay between developing multiple comorbidities in terms of both pathophysiologic and treatment interactions. Specifically, the typical situation is to develop a CPM for one particular outcome, but this fails to capture multi-outcome patterns evolving over time. With the rising emphasis on the prediction of multi-morbidity, there is a growing need for CPMs to simultaneously predict risks for each of multiple future outcomes. A common 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.
In this talk, Glen will introduce the notion of a multivariate (multi-outcome) CPM, present initial results on this topic and explore avenues for future work we are undertaking on this topic.
About the speaker
Glen Martin is a health data scientist undertaking multidisciplinary research at the intersection of mathematics, statistics, epidemiology and data science to conduct clinical investigations. Specifically, Glen is interesting in developing statistical methodology around clinical prediction models, to improve the development, validation and implementation of such models in electronic health records and clinical practice.
Please note that the time listed is Greenwich Mean Time (GMT)