title: Bayesian semiparametric modeling for matched case-control studies with multiple disease states.
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.
authors.affiliation: Department of Statistics, University of Florida, Gainesville, Florida 32611, USA. ssinha@stat.ufl.edu
authors.forename: Samiran
authors.initials: S
authors.lastname: Sinha
authors.forename: Bhramar
authors.initials: B
authors.lastname: Mukherjee
authors.forename: Malay
authors.initials: M
authors.lastname: Ghosh
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D016013:Likelihood Functions; D016015:Logistic Models; D008390:Markov Chains; D009010:Monte Carlo Method; D011247:Pregnancy