Bayesian analysis for partly linear Cox model with measurement error and time-varying covariate effect.
Bayesian analysis for partly linear Cox model with measurement error and time-varying covariate effect.
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DOI：
10.1002/sim.9531
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发表时间：
2022-10-15
影响因子：
2
通讯作者：
Huang, Hanwen
中科院分区：
文献类型：
--
作者：
Pan, Anqi;Song, Xiao;Huang, Hanwen
关键词：
考克斯比例风险模型通常用于估计至事件发生时间和协变量之间的相关性。在比例风险假设下，假设协变量效应在研究随访期间保持不变。当存在测量误差时，在考克斯比例风险模型中调整误差污染协变量的常用估计方法假设协变量的真实函数是参数化的和指定的。我们考虑半参数部分线性考克斯模型，该模型允许风险依赖于误差污染协变量和具有时变效应的无误差协变量的未指定函数，同时放松了对误差污染协变量的函数形式的假设，并允许无误差协变量的非恒定效应。我们采用贝叶斯方法并通过B-样条近似未指定的函数。仿真研究进行评估所提出的方法的有限样本性能。结果表明，我们提出的方法具有良好的统计性能。所提出的方法也说明了应用程序的数据从艾滋病临床试验组协议175。
The Cox proportional hazards model is commonly used to estimate the association between time‐to‐event and covariates. Under the proportional hazards assumption, covariate effects are assumed to be constant in the follow‐up period of study. When measurement error presents, common estimation methods that adjust for an error‐contaminated covariate in the Cox proportional hazards model assume that the true function on the covariate is parametric and specified. We consider a semiparametric partly linear Cox model that allows the hazard to depend on an unspecified function of an error‐contaminated covariate and an error‐free covariate with time‐varying effect, which simultaneously relaxes the assumption on the functional form of the error‐contaminated covariate and allows for nonconstant effect of the error‐free covariate. We take a Bayesian approach and approximate the unspecified function by a B‐spline. Simulation studies are conducted to assess the finite sample performance of the proposed approach. The results demonstrate that our proposed method has favorable statistical performance. The proposed method is also illustrated by an application to data from the AIDS Clinical Trials Group Protocol 175.
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DOI：
10.1007/978-3-7908-2413-1_8
发表时间：
2010-01-01
期刊：
STATISTICAL MODELLING AND REGRESSION STRUCTURES
影响因子：
--
作者：
Kneib, Thomas;Brezger, Andreas;Crainiceanu, Ciprian M.
通讯作者：
Crainiceanu, Ciprian M.
影响因子：
158.5
作者：
Hammer, SM;Katzenstein, DA;Salgo, M
通讯作者：
Salgo, M
影响因子：
1
作者：
Hu, CC;Lin, DY
通讯作者：
Lin, DY
影响因子：
3.7
作者：
Cheng YJ;Crainiceanu CM
通讯作者：
Crainiceanu CM
影响因子：
1
作者：
Cai, ZW;Sun, YQ
通讯作者：
Sun, YQ