Estimating Incident Population Distribution from Prevalent Data
Article first published online: 7 FEB 2012
© 2012, The International Biometric Society
Volume 68, Issue 2, pages 521–531, June 2012
How to Cite
Chan, K. C. G. and Wang, M.-C. (2012), Estimating Incident Population Distribution from Prevalent Data. Biometrics, 68: 521–531. doi: 10.1111/j.1541-0420.2011.01708.x
- Issue published online: 26 JUN 2012
- Article first published online: 7 FEB 2012
- Received June 2010. Revised September 2011. Accepted September 2011.
- Accelerated failure time model;
- Cross-sectional sampling;
- Left truncation;
- Proportional hazards model
Summary A prevalent sample consists of individuals who have experienced disease incidence but not failure event at the sampling time. We discuss methods for estimating the distribution function of a random vector defined at baseline for an incident disease population when data are collected by prevalent sampling. Prevalent sampling design is often more focused and economical than incident study design for studying the survival distribution of a diseased population, but prevalent samples are biased by design. Subjects with longer survival time are more likely to be included in a prevalent cohort, and other baseline variables of interests that are correlated with survival time are also subject to sampling bias induced by the prevalent sampling scheme. Without recognition of the bias, applying empirical distribution function to estimate the population distribution of baseline variables can lead to serious bias. In this article, nonparametric and semiparametric methods are developed for distribution estimation of baseline variables using prevalent data.