Conditional estimation for generalized linear models when covariates are subject-specific parameters in a mixed model for longitudinal measurements
Conditional estimation for generalized linear models when covariates are subject-specific parameters in a mixed model for longitudinal measurements
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DOI：
10.1111/j.0006-341x.2004.00170.x
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发表时间：
2004-03-01
期刊：
影响因子：
1.9
通讯作者：
Davidian, M
中科院分区：
文献类型：
--
作者：
Li, EN;Zhang, DW;Davidian, M
主要终点与连续应答的纵向特征之间的关系通常令人感兴趣，相关框架是广义线性模型，其协变量是纵向测量的线性混合模型中的受试者特异性随机效应。通过从个体回归拟合中估算受试者特异性效应的朴素实现产生有偏差的推断，并且已经提出了几种减少这种偏差的方法。这需要对随机效应进行参数（正态性）假设，这可能不现实。采用Stefanski和卡罗尔（1987，Biometrika 74，703-716）的策略，我们提出了广义线性模型参数的估计，不需要对随机效应进行假设，并且无论真实分布如何，都可以得到一致的推断。该方法通过模拟和应用程序来说明过渡到更年期的妇女的骨密度的研究。
The relationship between a primary endpoint and features of longitudinal profiles of a continuous response is often of interest, and a relevant framework is that of a generalized linear model with covariates that are subject-specific random effects in a linear mixed model for the longitudinal measurements. Naive implementation by imputing subject-specific effects from individual regression fits yields biased inference, and several methods for reducing this bias have been proposed. These require a parametric (normality) assumption on the random effects, which may be unrealistic. Adapting a strategy of Stefanski and Carroll (1987, Biometrika 74, 703-716), we propose estimators for the generalized linear model parameters that require no assumptions on the random effects and yield consistent inference regardless of the true distribution. The methods are illustrated via simulation and by application to a study of bone mineral density in women transitioning to menopause.