Title: Bayesian significance test for discriminating between survival distributions,
Abstract: An evaluation of FBST, Fully Bayesian Significance Test, restricted to
survival models is the main objective of the present paper. A Survival
distribution should be chosen among the tree celebrated ones, lognormal, gamma,
and Weibull. For this discrimination, a linear mixture of the three
distributions, for which the mixture weights are defined by a Dirichlet
distribution of order three, is an important tool: the FBST is used to test the
hypotheses defined on the mixture weights space. Another feature of the paper
is that all three distributions are reparametrized in that all the six
parameters - two for each distribution - are written as functions of the mean
and the variance of the population been studied. Note that the three
distributions share the same two parameters in the mixture model. The mixture
density has then four parameters, the same two for the three discriminating
densities and two for the mixture weights. Some numerical results from
simulations with some right-censored data are considered. The
lognormal-gamma-Weibull model is also applied to a real study with dataset
being composed by patient's survival times of patients in the end-stage of
chronic kidney failure subjected to hemodialysis procedures; data from Rio de
Janeiro hospitals. The posterior density of the weights indicates an order of
the mixture weights and the FBST is used for discriminating between the three
survival distributions.
Keywords: Model choice; Separate Models; Survival distributions; Mixture
model; Significance test; FBST