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Nonparametric estimation of the probability of illness in the illness-death model under cross-sectional sampling

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  • M. Mandel
  • R. Fluss

Abstract

Cross-sectional sampling is an attractive design that saves resources but results in biased data. For proper inference, one should first discover the bias function and then weigh observations appropriately. We consider cross-sectioning of the illness-death model with the aim of estimating the probability of visiting the illness state before death. We develop simple consistent and asymptotically normal estimators under various assumptions on the model and data collection and, in particular, compare designs with and without a follow-up. These designs are common in surveillance of hospital acquired infections, but estimators currently in use do not properly correct the bias. Copyright 2009, Oxford University Press.

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  • M. Mandel & R. Fluss, 2009. "Nonparametric estimation of the probability of illness in the illness-death model under cross-sectional sampling," Biometrika, Biometrika Trust, vol. 96(4), pages 861-872.
    • Handle: RePEc:oup:biomet:v:96:y:2009:i:4:p:861-872
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    File URL: http://hdl.handle.net/10.1093/biomet/asp046
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    Cited by:

    1. Marco Carone & Masoud Asgharian & Nicholas P. Jewell, 2014. "Estimating the Lifetime Risk of Dementia in the Canadian Elderly Population Using Cross-Sectional Cohort Survival Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 24-35, March.
    2. Micha Mandel & Yosef Rinott, 2012. "Cross-Sectional Sampling, Bias, Dependence, and Composite Likelihood," Discussion Paper Series dp614, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    3. Mei-Cheng Wang & Yuxin Zhu, 2022. "Bias correction via outcome reassignment for cross-sectional data with binary disease outcome," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 659-674, October.

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