A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data
A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data
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DOI：
10.1080/01621459.2017.1356316
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发表时间：
2018-01-01
影响因子：
3.7
通讯作者：
Hanson, Timothy
中科院分区：
文献类型：
--
作者：
Zhou, Haiming;Hanson, Timothy
一个全面的，统一的方法来建模任意删失的空间生存数据的三个最常用的半参数模型：比例风险，比例优势，加速失效时间。与许多其他方法不同，所有方式的删失生存时间同时容纳，包括未删失，区间删失，当前状态，左和右删失，以及这些的混合。左截断的数据也容纳导致模型的时间依赖性协变量。地理参考（位置精确观察）和区域观察（位置已知的地理单位，如县）的空间位置进行处理;正式的变量选择，使模型选择特别容易。用条件Cox-Snell残差图评估模型拟合，通过对数伪边缘似然（LPML）和偏差信息准则（DIC）进行模型选择。基线生存率采用新的转换后的伯恩斯坦多项式先验模型。所有模型都通过一个新的函数来拟合，该函数调用R包spBayesSurv中的高效编译C++。该方法广泛地说明了模拟和真实的数据应用。一个重要的发现是，比例几率和加速失效时间模型往往比常用的比例风险模型拟合得更好。
A comprehensive, unified approach to modeling arbitrarily censored spatial survival data is presented for the three most commonly used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. Unlike many other approaches, all manner of censored survival times are simultaneously accommodated including uncensored, interval censored, current-status, left and right censored, and mixtures of these. Left-truncated data are also accommodated leading to models for time-dependent covariates. Both georeferenced (location exactly observed) and areally observed (location known up to a geographic unit such as a county) spatial locations are handled; formal variable selection makes model selection especially easy. Model fit is assessed with conditional Cox-Snell residual plots, and model choice is carried out via log pseudo marginal likelihood (LPML) and deviance information criterion (DIC). Baseline survival is modeled with a novel transformed Bernstein polynomial prior. All models are fit via a new function which calls efficient compiled C++ in the R package spBayesSurv. The methodology is broadly illustrated with simulations and real data applications. An important finding is that proportional odds and accelerated failure time models often fit significantly better than the commonly used proportional hazards model.