Semiparametric Likelihood Estimation in the Clayton–Oakes Failure Time Model |
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Authors: | D V Glidden & S G Self |
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Institution: | University of California, San Francisco,;Fred Hutchinson Cancer Research Center |
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Abstract: | Multivariate failure time data arise when the sample consists of clusters and each cluster contains several possibly dependent failure times. The Clayton–Oakes model (Clayton, 1978; Oakes, 1982) for multivariate failure times characterizes the intracluster dependence parametrically but allows arbitrary specification of the marginal distributions. In this paper, we discuss estimation in the Clayton–Oakes model when the marginal distributions are modeled to follow the Cox (1972) proportional hazards regression model. Parameter estimation is based on an approximate generalized maximum likelihood estimator. We illustrate the model's application with example datasets. |
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Keywords: | frailty model multivariate failure time non-parametric maximum likelihood |
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