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Asymptotic justification of maximum likelihood estimation for the proportional excess hazard model in analysis of cancer registry data
Population-based cancer registry studies are conducted to investigate the various cancer question and have important impacts on cancer control. In order to investigate cancer prognosis from cancer registry data, it is necessary to adjust the effect of deaths from other causes, since cancer registry...
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Published in: | Japanese journal of statistics and data science 2023-06, Vol.6 (1), p.337-359 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Population-based cancer registry studies are conducted to investigate the various cancer question and have important impacts on cancer control. In order to investigate cancer prognosis from cancer registry data, it is necessary to adjust the effect of deaths from other causes, since cancer registry data include deaths from causes other than cancer. To correct for the effect of deaths from other causes, excess hazard models are often used. The concept of the excess hazard model is that the hazard function for any death in a cancer registry population is the sum of the hazard for cancer deaths, refer to the excess hazard, and the hazard for deaths from other causes. The Cox proportional hazard model for the excess hazard has been developed, and for this model, Perme et al. (Biostatistics 10:136–146, 2009) proposed the inference procedure of the regression coefficients using the techniques of the EM algorithm to compute the maximum likelihood estimator. In this article, we present the large sample properties for the maximum likelihood estimator. We introduce a consistent estimator of the variance for the regression coefficients based on the technique of the semiparametric theory and the consistency and the asymptotic normality of the estimator. The empirical property of variance estimator is investigated by the finite sample simulation studies. We also apply the variance estimator to cancer registry data for stomach, lung, and liver cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database in U.S. |
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ISSN: | 2520-8756 2520-8764 |
DOI: | 10.1007/s42081-023-00190-6 |