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Greenberg, R.A. and Bayard, S. (1974) Selecting concomitant variables using a likelihood ratio step-down procedure and a method of testing goodness of fit in an exponential survival model. Biometrics, 30(4), 601-608.

has been cited by the following article:

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

  AUTHORS: Dinesh Kumar

  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 (.).

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.3 No.7, July 15, 2010

  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.