Bayesian analysis of the differences of count data.
Paired count data usually arise in medicine when before and after treatment measurements are considered. In the present paper we assume that the correlated paired count data follow a bivariate Poisson distribution in order to derive the distribution of their difference. The derived distribution is shown to be the same as the one derived for the difference of the independent Poisson variables, thus recasting interest on the distribution introduced by Skellam. Using this distribution we remove correlation, which naturally exists in paired data, and we improve the quality of our inference by using exact distributions instead of normal approximations. The zero-inflated version is considered to account for an excess of zero counts. Bayesian estimation and hypothesis testing for the models considered are discussed. An example from dental epidemiology is used to illustrate the proposed methodology.
Karlis
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D
Department of Statistics, Athens University of Economics and Business, 76, Patission Str., 10434 Athens, Greece. karlis@aueb.gr
Ntzoufras
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I
D000465:Algorithms; D001499:Bayes Theorem; D001938:Brazil / Q000453:epidemiology; D002648:Child; D004245:DMF Index; D003627:Data Interpretation, Statistical; D003731:Dental Caries / Q000453:epidemiology / Q000517:prevention & control; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D016012:Poisson Distribution; D014070:Tooth / Q000473:pathology