Catalano P J
Division of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Stat Med. 1997 Apr 30;16(8):883-900. doi: 10.1002/(sici)1097-0258(19970430)16:8<883::aid-sim542>3.0.co;2-e.
Simultaneous observation of continuous and ordered categorical outcomes for each subject is common in biomedical research but multivariate analysis of the data is complicated by the multiple data types. Here we construct a model for the joint distribution of bivariate continuous and ordinal outcomes by applying the concept of latent variables to a multivariate normal distribution. The approach is then extended to allow for clustering of the bivariate outcomes. The model can be parameterized in a way that allows writing the joint distribution as a product of a standard random effects model for the continuous variable and a correlated cumulative probit model for the ordinal outcome. This factorization suggests convenient parameter estimation using estimating equations. Foetal weight and malformation data from a developmental toxicity experiment illustrate the results.
在生物医学研究中，对每个受试者同时观察连续和有序分类结果很常见，但由于数据类型多样，对这些数据进行多变量分析会变得复杂。在此，我们通过将潜在变量的概念应用于多元正态分布，构建了一个用于双变量连续和有序结果联合分布的模型。然后，该方法被扩展以允许双变量结果的聚类。该模型可以以一种允许将联合分布写成连续变量的标准随机效应模型和有序结果的相关累积概率单位模型的乘积的方式进行参数化。这种因式分解表明使用估计方程进行方便的参数估计。来自发育毒性实验的胎儿体重和畸形数据说明了结果。
