A correlated probit model for joint modeling of clustered binary and continuous responses
A correlated probit model for joint modeling of clustered binary and continuous responses
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DOI：
10.1198/016214501753208762
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发表时间：
2001-09-01
影响因子：
3.7
通讯作者：
Agresti, A
中科院分区：
文献类型：
--
作者：
Gueorguieva, RV;Agresti, A
连续和离散响应变量联合建模的一个困难是缺乏自然的多元分布。对于二元和连续响应的聚类观察的联合建模，我们研究了一个相关概率模型，该模型具有二元响应的潜在正态潜变量。 Catalano 和 Ryan 将模型分解为边际成分和条件成分，并使用广义估计方程方法来估计效果。我们提出了一种蒙特卡洛期望条件最大化算法，用于查找混合模型本身的最大似然估计，扩展和加速具有二元响应的模型的算法。我们通过发育毒性研究来证明该方法，该研究测量了几窝小鼠的胎儿体重和二元畸形状态。模拟研究表明，联合拟合相对于响应变量的单独拟合的效率增益主要发生在簇内响应之间具有强相关性的小数据集上。
A difficulty in joint modeling of continuous and discrete response variables is the lack of a natural multivariate distribution. For joint modeling of clustered observations on binary and continuous responses, we study a correlated probit model that has an underlying normal latent variable for the binary responses. Catalano and Ryan have factored the model into a marginal and a conditional component and used generalized estimating equations methodology to estimate the effects. We propose a Monte Carlo expectation-conditional maximization algorithm for finding maximum likelihood estimates of the mixed model itself, extending and accelerating an algorithm for models with binary responses. We demonstrate the methodology with a developmental toxicity study measuring fetal weight and a binary malformation status for several litters of mice. A simulation study suggests that efficiency gains of joint fittings over separate fittings of the response variables occur mainly for small datasets with strong correlations between the responses within cluster.