Statistical inference for generalized case-cohort design under the proportional hazards model with parameter constraints |
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Authors: | Yingli Pan Yanyan Liu |
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Affiliation: | 1. School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, Hubei, China;2. School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China |
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Abstract: | ABSTRACTThe generalized case-cohort design is widely used in large cohort studies to reduce the cost and improve the efficiency. Taking prior information of parameters into consideration in modeling process can further raise the inference efficiency. In this paper, we consider fitting proportional hazards model with constraints for generalized case-cohort studies. We establish a working likelihood function for the estimation of model parameters. The asymptotic properties of the proposed estimator are derived via the Karush-Kuhn-Tucker conditions, and their finite properties are assessed by simulation studies. A modified minorization-maximization algorithm is developed for the numerical calculation of the constrained estimator. An application to a Wilms tumor study demonstrates the utility of the proposed method in practice. |
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Keywords: | Constrained estimation Generalized case-cohort design Karush-Kuhn-Tucker conditions Minorization-maximization algorithm Proportional hazards model |
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