|Title||BAYESIAN INFERENCE OF HIDDEN GAMMA WEAR PROCESS MODEL FOR SURVIVAL DATA WITH TIES.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Sinha, Arijit, Zhiyi Chi, and Ming-Hui Chen|
|Date Published||2015 Oct|
Survival data often contain tied event times. Inference without careful treatment of the ties can lead to biased estimates. This paper develops the Bayesian analysis of a stochastic wear process model to fit survival data that might have a large number of ties. Under a general wear process model, we derive the likelihood of parameters. When the wear process is a Gamma process, the likelihood has a semi-closed form that allows posterior sampling to be carried out for the parameters, hence achieving model selection using Bayesian deviance information criterion. An innovative simulation algorithm via direct forward sampling and Gibbs sampling is developed to sample event times that may have ties in the presence of arbitrary covariates; this provides a tool to assess the precision of inference. An extensive simulation study is reported and a data set is used to further illustrate the proposed methodology.
|Alternate Journal||Stat Sin|
|Original Publication||Bayesian inference of hidden gamma wear process model for survival data with ties.|
|PubMed Central ID||PMC4643298|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 GM070335 / GM / NIGMS NIH HHS / United States
BAYESIAN INFERENCE OF HIDDEN GAMMA WEAR PROCESS MODEL FOR SURVIVAL DATA WITH TIES.