Selecting the Best Cancer Risk Prediction Models

Grant number: 1129136 | Funding period: 2017 - 2019

Completed

Abstract

Risk prediction models incorporating multiple risk factors (including genetic markers) are a recognised method to identify individuals at high risk of developing breast or colorectal cancer, but it is uncertain which model(s) currently perform best in a population setting. We aim to compare the predictive ability of each available model. Knowing which model performs best will facilitate early diagnosis, reduce overall costs by better targeting interventions and improve cancer survival.

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University of Melbourne Researchers