Bayesian semiparametric modeling for matched case-control studies with multiple disease states |
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Authors: | Sinha Samiran Mukherjee Bhramar Ghosh Malay |
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Institution: | Department of Statistics, University of Florida, Gainesville, Florida 32611, USA. ssinha@stat.ufl.edu |
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Abstract: | We present a Bayesian approach to analyze matched "case-control" data with multiple disease states. The probability of disease development is described by a multinomial logistic regression model. The exposure distribution depends on the disease state and could vary across strata. In such a model, the number of stratum effect parameters grows in direct proportion to the sample size leading to inconsistent MLEs for the parameters of interest even when one uses a retrospective conditional likelihood. We adopt a semiparametric Bayesian framework instead, assuming a Dirichlet process prior with a mixing normal distribution on the distribution of the stratum effects. We also account for possible missingness in the exposure variable in our model. The actual estimation is carried out through a Markov chain Monte Carlo numerical integration scheme. The proposed methodology is illustrated through simulation and an example of a matched study on low birth weight of newborns (Hosmer, D. A. and Lemeshow, S., 2000, Applied Logistic Regression) with two possible disease groups matched with a control group. |
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Keywords: | Conditional inference Dirichlet mixture Exponential family Gibbs sampling |
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