Projecting Population Risk with Time-to-Event Data

• Activity Number: 470
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Dandan Liu*+ and Li Hsu and Yingye Zheng
• Companies: Vanderbilt University and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
• Keywords: absolute risk ; attributable risk ; projecting individualized risk ; external sources ; time-to-event ; semiparametric

Abstract

Accurate and individualized risk prediction is fundamental for successful prevention and intervention of many chronic diseases such as cancer and cardiovascular diseases. Cohort data provide useful resources to estimate the population risk, however, frequently studies used for deriving prediction models may come from heterogeneous populations with baseline risks differing from that of the general population. For example, a healthy cohort effect has often been observed in large scale cohort studies. In this situation, while the relative risk may be generalizable to the population from which the cohort is sampled, the absolute risk can be biased. We propose an approach that borrows the external disease incidence information to estimate the absolute risk, through the time-dependent attributable risk function. Both analytical and simulation results show that the proposed approach has a smaller bias than the cohort-based absolute risk estimators, and the estimator is more efficient. An application of the proposed method to a real data set is also provided.