Bayesian estimation of survival functions under stochastic precedence

被引:4
作者
Chen, Z
Dunson, DB
机构
[1] Univ Penn, Dept Biostat & Epidemiol, Philadelphia, PA 19104 USA
[2] NIEHS, Biostat Branch, Res Triangle Pk, NC 27709 USA
关键词
censoring; order restriction; categorical data; Gibbs sampler; stochastic order; survival analysis; Kaplan-Meier estimates;
D O I
10.1023/B:LIDA.0000030201.12943.13
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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 less than or equal to z) greater than or equal to Pr(Y less than or equal to 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 less than or equal to Y) greater than or equal to 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.
引用
收藏
页码:159 / 173
页数:15
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