Bayesian and hierarchical Bayesian analysis of response - time data with concomitant variables

Dinesh Kumar

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DOI: 10.4236/jbise.2010.37095                

Abstract

This paper considers the Bayes and hierarchical Bayes approaches for analyzing clinical data on response times with available values for one or more concomitant variables. Response times are assumed to follow simple exponential distributions, with a different parameter for each patient. The analyses are carried out in case of progressive censoring assuming squared error loss function and gamma distribution as priors and hyperpriors. The possibilities of using the methodology in more general situations like dose- response modeling have also been explored. Bayesian estimators derived in this paper are applied to lung cancer data set with concomitant variables.

Keywords

Bayes Estimator; Bayesian Posterior Density; Gamma Prior Density (GPD); Hierarchical Bayes Estimator; Hyperprior; Noninformative Prior Quasi-Density (NPQD); Progressive Censoring; Squared Error Loss Function (SELF); Whittaker Function W s1, s2 (.).

Conflicts of Interest

The authors declare no conflicts of interest.

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