Bayesian Log-Rank Test.
Comparison of two survival curves is a fundamental problem in survival analysis. Although abundant frequentist methods have been developed for comparing survival functions, inference procedures from the Bayesian perspective are rather limited. In this article, we extract the quantity of interest fro...
| Published in: | American Statistician Vol. 77; no. 3; pp. 292 - 301 |
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| Main Authors: | , , |
| Format: | Article |
| Published: |
Taylor & Francis Ltd
Aug2023
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=169847631&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 169847631 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Aug2023 vid: 77 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 169847631 10.1080/00031305.2022.2161637 ppf: 292 ppct: 9 formats: tig: atl: Bayesian Log-Rank Test. aug: au: Gu, Jiaqi Zhang, Yan Yin, Guosheng affil: Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong su: Log-rank test Gibbs sampling Monte Carlo method Survival analysis (Biometry) Sampling (Process) Censoring (Statistics) sug: subj: Log-rank test Gibbs sampling Monte Carlo method Survival analysis (Biometry) Sampling (Process) Censoring (Statistics) keyword: Censored data Mixture of Dirichlet processes Monte Carlo Survival function Censored data Mixture of Dirichlet processes Monte Carlo Survival function ab: Comparison of two survival curves is a fundamental problem in survival analysis. Although abundant frequentist methods have been developed for comparing survival functions, inference procedures from the Bayesian perspective are rather limited. In this article, we extract the quantity of interest from the classic log-rank test and propose its Bayesian counterpart. Monte Carlo methods, including a Gibbs sampler and a sequential importance sampling procedure, are developed to draw posterior samples of survival functions and a decision rule of hypothesis testing is constructed for making inference. Via simulations and real data analysis, the proposed Bayesian log-rank test is shown to be asymptotically equivalent to the classic one when noninformative prior distributions are used, which provides a Bayesian interpretation of the log-rank test. When using the correct prior information from historical data, the Bayesian log-rank test is shown to outperform the classic one in terms of power. R codes to implement the Bayesian log-rank test are also provided with step-by-step instructions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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