Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data.
Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data.
复制标题
DOI：
10.1186/1471-2288-10-69
复制
发表时间：
2010-07-29
影响因子：
4
通讯作者：
Chevret S
中科院分区：
文献类型：
--
作者：
Deslandes E;Chevret S
在临床试验中，特别是在癌症和艾滋病中，越来越多地考虑纵向和生存数据的联合建模。对于入住重症监护室（ICU）的危重患者，由于纵向评分与因死亡或从ICU活出院而导致的退学过程之间可能存在关联，因此此类模型似乎对治疗对严重程度评分的影响的调查也很感兴趣。然而，在这种相互竞争的风险设置中，只使用了针对多种故障类型数据的特定原因的危险子模型。我们提出了一个联合模型，该模型由纵向结果的线性混合效应子模型和竞争风险生存数据的比例子分布风险子模型组成，并通过潜在随机效应联系在一起。利用吉布斯抽样的马尔可夫链蒙特卡罗技术估计模型未知参数的联合后验分布。研究了所提出的方法，并在模拟中将其与具有原因特异性危险子模型的联合模型进行了比较，并将其应用于1,401例ICU患者的严重程度评分、出院时间和死亡时间的重复测量数据集。在忽略ICU死亡和ICU活出院的潜在信息性退出时，观察治疗相互作用的时间对平均SOFA评分的演变。相比之下，当对信息性辍学的特定原因风险进行建模时，这一点不再重要。当使用Fine和Gray模型对子分布危险进行退学过程建模时，这种治疗相互作用的时间与治疗对死亡危险的影响的证据一起持续存在。在竞争风险与纵向反应的联合建模中，对竞争风险结果的处理差异似乎转化为对纵向结果的治疗效果的估计差异。在分析之前，应该仔细定义这样的建模策略。
Joint modeling of longitudinal and survival data has been increasingly considered in clinical trials, notably in cancer and AIDS. In critically ill patients admitted to an intensive care unit (ICU), such models also appear to be of interest in the investigation of the effect of treatment on severity scores due to the likely association between the longitudinal score and the dropout process, either caused by deaths or live discharges from the ICU. However, in this competing risk setting, only cause-specific hazard sub-models for the multiple failure types data have been used. We propose a joint model that consists of a linear mixed effects submodel for the longitudinal outcome, and a proportional subdistribution hazards submodel for the competing risks survival data, linked together by latent random effects. We use Markov chain Monte Carlo technique of Gibbs sampling to estimate the joint posterior distribution of the unknown parameters of the model. The proposed method is studied and compared to joint model with cause-specific hazards submodel in simulations and applied to a data set that consisted of repeated measurements of severity score and time of discharge and death for 1,401 ICU patients. Time by treatment interaction was observed on the evolution of the mean SOFA score when ignoring potentially informative dropouts due to ICU deaths and live discharges from the ICU. In contrast, this was no longer significant when modeling the cause-specific hazards of informative dropouts. Such a time by treatment interaction persisted together with an evidence of treatment effect on the hazard of death when modeling dropout processes through the use of the Fine and Gray model for sub-distribution hazards. In the joint modeling of competing risks with longitudinal response, differences in the handling of competing risk outcomes appear to translate into the estimated difference in treatment effect on the longitudinal outcome. Such a modeling strategy should be carefully defined prior to analysis.
登录
查看更多内容
DOI：
10.1093/biostatistics/1.4.465
发表时间：
2000-12-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Henderson, R;Diggle, P;Dobson, A
通讯作者：
Dobson, A
影响因子：
2
作者：
Li, Ning;Elashoff, Robert M.;Saver, Jeffrey
通讯作者：
Saver, Jeffrey
影响因子：
8.8
作者：
Bilotta, Federico;Caramia, Remo;Rosa, Giovanni
通讯作者：
Rosa, Giovanni
DOI：
10.1093/biostatistics/2.1.85
发表时间：
2001-03-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Fine, J P
通讯作者：
Fine, J P
影响因子：
2
作者：
Diggle, Peter J.;Sousa, Ines;Chetwynd, Amanda G.
通讯作者：
Chetwynd, Amanda G.