A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome.
A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome.
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DOI：
10.1007/s10260-010-0150-z
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发表时间：
2011-03-01
影响因子：
1
通讯作者：
Gordon, Mae
中科院分区：
文献类型：
--
作者：
Gao, Feng;Miller, J. Philip;Xiong, Chengjie;Beiser, Julia A.;Gordon, Mae
关键词：
在一些临床试验和流行病学研究中，研究人员对生物标志物的可变性是否能独立预测临床结果很感兴趣。这个问题通常通过naïve方法来解决，其中基于样本的估计（例如，标准偏差）被计算为“真实”可变性的替代品，然后在回归模型中作为假设没有测量误差的协变量使用。然而，众所周知，协变量的测量误差会导致对真实关联的低估。当由于每个主题的测量数量有限而导致精度较低时，低估的问题可能是实质性的。生存数据和纵向数据的联合分析使人们能够解释纵向数据中的测量误差，近年来受到了大量关注。在本文中，我们提出了一个联合模型来评估生物标志物变异性的预测作用。联合模型由两个相连的子模型组成，纵向数据为具有患者特异性方差的线性混合模型，生存数据为全参数威布尔分布，两个模型之间的关联由潜在高斯过程诱导。在贝叶斯框架下对联合模型中的参数进行估计，并在WinBUGS软件下使用马尔可夫链蒙特卡罗（MCMC）方法实现。该方法在高眼压治疗研究中进行了说明，以评估眼压的变异性是否是原发性开角型青光眼的独立危险因素。通过仿真研究对该方法的性能进行了评价。
In some clinical trials and epidemiologic studies, investigators are interested in knowing whether the variability of a biomarker is independently predictive of clinical outcomes. This question is often addressed via a naïve approach where a sample-based estimate (e.g., standard deviation) is calculated as a surrogate for the “true” variability and then used in regression models as a covariate assumed to be free of measurement error. However, it is well known that the measurement error in covariates causes underestimation of the true association. The issue of underestimation can be substantial when the precision is low because of limited number of measures per subject. The joint analysis of survival data and longitudinal data enables one to account for the measurement error in longitudinal data and has received substantial attention in recent years. In this paper we propose a joint model to assess the predictive effect of biomarker variability. The joint model consists of two linked sub-models, a linear mixed model with patient-specific variance for longitudinal data and a full parametric Weibull distribution for survival data, and the association between two models is induced by a latent Gaussian process. Parameters in the joint model are estimated under Bayesian framework and implemented using Markov chain Monte Carlo (MCMC) methods with WinBUGS software. The method is illustrated in the Ocular Hypertension Treatment Study to assess whether the variability of intraocular pressure is an independent risk of primary open-angle glaucoma. The performance of the method is also assessed by simulation studies.
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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：
158.5
作者：
IRIBARREN, C;SHARP, DS;PETROVITCH, H
通讯作者：
PETROVITCH, H
影响因子：
1.8
作者：
Guo, X;Carlin, BP
通讯作者：
Carlin, BP
影响因子：
2
作者：
Lyles, Robert H.;Manatunga, Amita K.;Cook, Curtiss B.
通讯作者：
Cook, Curtiss B.
影响因子：
1.9
作者：
HUGHES, MD
通讯作者：
HUGHES, MD