Semiparametric Bayesian estimation of quantile function for breast cancer survival data with cured fraction.
Semiparametric Bayesian estimation of quantile function for breast cancer survival data with cured fraction.
复制标题
具有治愈分数的乳腺癌生存数据的分位数函数的半参数贝叶斯估计。
DOI：
10.1002/bimj.201500111
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Sinha,Debjayoti
中科院分区：
文献类型：
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作者：
Gupta,Cherry;Cobre,Juliana;Polpo,Adriano;Sinha,Debjayoti
现有的治愈率生存模型通常不便于对具有特定协变量值的患者的生存分位数进行建模和估计。本文提出了一类新的治愈率模型，即两侧转换治愈率模型（TBSCRM），它可以用来对治愈率和生存分位数进行推断。我们通过马尔可夫链蒙特卡罗（MCMC）工具开发了关于治愈率和生存分位数的协变量影响的贝叶斯推断。我们还表明，在我们的模拟研究中，基于TBSCRM的贝叶斯方法优于现有的基于治愈率模型的方法，并应用于来自美国国家癌症研究所监测、流行病学和最终结果（SEER）数据库的乳腺癌生存数据。
Existing cure‐rate survival models are generally not convenient for modeling and estimating the survival quantiles of a patient with specified covariate values. This paper proposes a novel class of cure‐rate model, the transform‐both‐sides cure‐rate model (TBSCRM), that can be used to make inferences about both the cure‐rate and the survival quantiles. We develop the Bayesian inference about the covariate effects on the cure‐rate as well as on the survival quantiles via Markov Chain Monte Carlo (MCMC) tools. We also show that the TBSCRM‐based Bayesian method outperforms existing cure‐rate models based methods in our simulation studies and in application to the breast cancer survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database.