A Note on Risk Prediction for Case-Control Studies

Sherri Rose, Division of Biostatistics, University of California, Berkeley
Mark J. van der Laan, University of California - Berkeley

Abstract

We introduce a new method for prediction in case-control study designs, which is a simple extension of the work by van der Laan (2008). Case-control samples are biased since the proportion of cases in the sample is not the same as the population of interest. The case-control weighting for prediction proposed in this paper relies on knowledge of the true incidence probability P(Y=1) to eliminate the bias of the sampling design. In many practical settings, case-control weighting will outperform an existing method for prediction, intercept adjustment.