Abstract
Cross-sectional surveys are often used in epidemiological studies to identify subjects with a disease. When estimating the survival function from the onset of disease, this sampling mechanism introduces bias, which must be accounted for. If the onset times of the disease are assumed to be coming from a stationary Poisson process, this bias, which is caused by the sampling of prevalent rather than incident cases, is termed length bias. One-sample goodness-of-fit tests are proposed for right-censored length-biased data based on Kolmogorov and Cramér–von-Mises criteria. Approximate critical values, power, and behavior are investigated using Weibull, lognormal, and log-logistic models through simulation. Algorithms detailing how to efficiently generate right-censored length-biased survival data of these parametric forms are given. Finally, the test is used to evaluate the goodness of fit using length-biased survival data of patients with dementia from the Canadian study of health and aging. Evidence for different parametric forms between men and women is found, suggesting course of disease to vary between genders.
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Acknowledgment
The data used in this chapter were collected as part of the Canadian Study of Health and Aging. The core study was funded by the Seniors’ Independence Research Program, through the National Health Research and Development Program (NHRDP) of Health Canada (project no. 6606-3954-MC(S)). Additional funding was provided by Pfizer Canada Incorporated through the Medical Research Council/Pharmaceutical Manufacturers Association of Canada Health Activity Program, NHRDP (project no. 6603-1417-302(R)), Bayer Incorporated, and the British Columbia Health Research Foundation (projects no. 38 (93-2) and no. 34 (96-1)). The study was coordinated through the University of Ottawa and the Division of Aging and Seniors, Health Canada.
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Bergeron, PJ., Sucha, E., Younger, J. (2015). Goodness-of-Fit Tests for Length-Biased Right-Censored Data with Application to Survival with Dementia. In: Chen, Z., Liu, A., Qu, Y., Tang, L., Ting, N., Tsong, Y. (eds) Applied Statistics in Biomedicine and Clinical Trials Design. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-12694-4_20
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DOI: https://doi.org/10.1007/978-3-319-12694-4_20
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