McCulloch Charles
Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
Stat Methods Med Res. 2008 Feb;17(1):53-73. doi: 10.1177/0962280207081240. Epub 2007 Sep 13.
After a brief review of the use of latent variables to accommodate the correlation among multiple outcomes of mixed types, through theoretical and numerical calculation, the consequences of such a construction are quantified. The effects of including latent variables on marginal inference in these models are contrasted with the situation for jointly normal outcomes. A simulation study illustrates the efficiency and reduction in bias gains possible in using joint models, and analysis of an example from the field of osteoarthritis illustrates potential practical differences.
在简要回顾了使用潜在变量来处理混合类型多个结果之间的相关性之后，通过理论和数值计算，对这种构建的结果进行了量化。将这些模型中包含潜在变量对边际推断的影响与联合正态结果的情况进行了对比。一项模拟研究说明了使用联合模型可能实现的效率提高和偏差减少，对骨关节炎领域的一个例子进行分析则说明了潜在的实际差异。
