In this article, we develop a piecewise Poisson regression method to analyze survival data from complex sample surveys involving cluster-correlated, differential selection probabilities, and longitudinal responses, to conveniently draw inference on absolute risks in time intervals that are prespecified by investigators. Extensive simulations evaluate the developed methods with extensions to multiple covariates under various complex sample designs, including stratified sampling, sampling with selection probability proportional to a measure of size (PPS), and a multi-stage cluster sampling. We applied our methods to a study of mortality in men diagnosed with prostate cancer in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial to investigate whether a biomarker available from biospecimens collected near time of diagnosis stratifies subsequent risk of death. Poisson regression coefficients and absolute risks of mortality (and the corresponding 95% confidence intervals) for prespecified age intervals by biomarker levels are estimated. We conclude with a brief discussion of the motivation, methods, and findings of the study.
Keywords: Absolute risks; Complex sampling designs; Marginal prediction; PLCO; Poisson regression; Probability proportional to a measure of size (PPS).
© 2015, The International Biometric Society.