Separate and joint modeling of longitudinal and event time data using standard computer packages
Separate and joint modeling of longitudinal and event time data using standard computer packages
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DOI：
10.1198/0003130042854
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发表时间：
2004-02-01
影响因子：
1.8
通讯作者：
Carlin, BP
中科院分区：
文献类型：
--
作者：
Guo, X;Carlin, BP
许多临床试验和其他医学和可靠性研究产生纵向（重复测量）和生存（事件发生时间）数据。有许多行之有效的方法可以单独分析这些数据，但当纵向变量与患者健康状况相关时，这些方法可能不合适，因此与生存终点相关（以及研究退出的可能性）。为了解决这个问题，早期的一篇文章提出了纵向和生存数据的联合模型，通过EM算法获得最大似然估计。假设纵向和生存反应是独立的，给出了一个潜在的二元高斯过程和可用的协变量。我们开发了这种方法的完全贝叶斯版本，通过马尔可夫链蒙特卡罗（MCMC）方法实现。我们使用该方法对一项艾滋病临床试验的纵向和生存数据进行了联合建模，该试验比较了两种治疗方法，双danosine （ddI）和zalcitabine （ddC）。尽管模型很复杂，但我们发现使用WinBUGS软件实现和理解它相对简单。我们将我们的结果与SAS Procs MIXED， NLMIXED， PHREG和LIFEREG中现成的替代方法以及这些传统的分离似然方法的贝叶斯类似物获得的结果进行了比较。联合贝叶斯方法似乎提供了显著改进和增强的中位生存时间和其他感兴趣的参数的估计，以及更简单的编码和可比较的运行时间。
Many clinical trials and other medical and reliability studies generate both longitudinal (repeated measurement) and survival (time to event) data. Many well-established methods exist for analyzing such data separately, but these may be inappropriate when the longitudinal variable is correlated with patient health status, hence the survival endpoint (as well as the possibility of study dropout). To remedy this, an earlier article proposed a joint model for longitudinal and survival data, obtaining maximum likelihood estimates via the EM algorithm. The longitudinal and survival responses are assumed independent given a linking latent bivariate Gaussian process and available covariates. We develop a fully Bayesian version of this approach, implemented via Markov chain Monte Carlo (MCMC) methods. We use the approach to jointly model the longitudinal and survival data from an AIDS clinical trial comparing two treatments, didanosine (ddI) and zalcitabine (ddC). Despite the complexity of the model, we find it to be relatively straightforward to implement and understand using the WinBUGS software. Wee compare our results to those obtained from readily available alternatives in SAS Procs MIXED, NLMIXED, PHREG, and LIFEREG, as well as Bayesian analogues of these traditional separate likelihood methods. The joint Bayesian approach appears to offer significantly improved and enhanced estimation of median survival times and other parameters of interest, as well as simpler coding and comparable runtimes.