Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.
Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.
复制标题
使用随机效应 Tobit 回归模型估计聚类和审查结果的总体暴露效果。
DOI：
10.1002/sim.7045
复制
发表时间：
2016
影响因子：
2
通讯作者：
Griswold,MichaelE
中科院分区：
文献类型：
--
作者：
Wang,Wei;Griswold,MichaelE
随机效应Tobit模型是一种回归模型，适用于结果变量的左和/或右审查和簇内依赖。随机效应Tobit模型的回归系数对构建的潜在因变量有条件解释，不提供对原始结果量表的总体暴露效应的推断。边缘随机效应模型（MREM）允许基于似然估计聚类数据的边缘平均参数。对于随机效应Tobit模型，我们将MREM扩展到对随机效应和截尾响应的正态空间和边界分量进行边缘化，以估计总体水平上的暴露效应。我们还扩展了“平均预测值”方法，通过整合随机效应来估计指定参考组中处于不同暴露状态的每个人的模型预测边际均值，然后使用计算出的差异来评估总体暴露效应。利用高斯-埃尔米特正交的拟牛顿优化算法，提出了最大似然估计来逼近随机效应的积分。我们使用这些方法仔细分析了两个真实的数据集。版权所有©2016 John Wiley & Sons， Ltd。
The random effect Tobit model is a regression model that accommodates both left‐ and/or right‐censoring and within‐cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood‐based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the ‘Average Predicted Value’ method to estimate the model‐predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi‐Newton optimization algorithm with Gauss–Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd.