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Bayesian Semiparametric Modeling for Matched Case-Control Studies with Multiple Disease States
Samiran Sinha, Bhramar Mukherjee and Malay Ghosh
Vol. 60, No. 1 (Mar., 2004), pp. 41-49
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/3695550
Page Count: 9
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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.
Biometrics © 2004 International Biometric Society