28 May 2015 10:00am

Developing risk prediction models using nested case-control data: methods and applications

Event Location
Agus Salim
Dr Agus Salim is Associate Professor in Biostatistics with the Melbourne School of Global and Population Health (joint with School of Mathematics and Statistics), The University of Melbourne. Agus has...

In the last 10-15 years, there has been an explosion of prediction models developed to predict risk of various diseases. The main objective of a risk prediction model is to predict the absolute risk of a disease. For this reason, cohort study design has been used in almost all studies that build or compare risk prediction models because cheaper alternatives such as case-control study cannot be used to validly estimate absolute risk.

In this talk, I will examine the feasibility of estimating absolute risk using nested case-control data. Two approaches will be compared using both a simulated and real dataset from a nested case-control study of coronary heart disease conducted within the Singaporean Chinese Health Study (SCHS) cohort. Finally, I will demonstrate how the methods can be used to investigate the usefulness of two biomarkers, highly-sensitive C-reactive protein (CRP) and serum creatinine to improve prediction of coronary heart disease (CHD) risk, on top of the traditional risk factors used by the Adult Treatment Panel III (ATP-III) risk calculator.