Bayesian estimation of survival functions under stochastic precedence.
Journal Article (Journal Article)
When estimating the distributions of two random variables, X and Y, investigators often have prior information that Y tends to be bigger than X. To formalize this prior belief, one could potentially assume stochastic ordering between X and Y, which implies Pr(X < or = z) > or = Pr(Y < or = z) for all z in the domain of X and Y. Stochastic ordering is quite restrictive, though, and this article focuses instead on Bayesian estimation of the distribution functions of X and Y under the weaker stochastic precedence constraint, Pr(X < or = Y) > or = 0.5. We consider the case where both X and Y are categorical variables with common support and develop a Gibbs sampling algorithm for posterior computation. The method is then generalized to the case where X and Y are survival times. The proposed approach is illustrated using data on survival after tumor removal for patients with malignant melanoma.
Full Text
Duke Authors
Cited Authors
- Chen, Z; Dunson, DB
Published Date
- June 2004
Published In
Volume / Issue
- 10 / 2
Start / End Page
- 159 - 173
PubMed ID
- 15293630
Electronic International Standard Serial Number (EISSN)
- 1572-9249
International Standard Serial Number (ISSN)
- 1380-7870
Digital Object Identifier (DOI)
- 10.1023/b:lida.0000030201.12943.13
Language
- eng