Department of Biostatistics, UCLA School of Public Health, University of California, Los Angeles, CA 90095-1772,USA.
Interface Focus. 2011 Dec 6;1(6):886-94. doi: 10.1098/rsfs.2011.0041. Epub 2011 Aug 31.
We propose a Bayesian multivariate model in which a single linear combination of the covariates predict multiple outcomes simultaneously. The single linear combination is a data-derived score along the lines of the Apache or Charlson index scores for critically ill patients, the Karnofsky or Eastern Cooperative Oncology Group score for cancer patients or Euro-score for cardiac patients that may be used to predict multiple outcomes. Outcomes may be discrete or continuous and we use a composition of generalized linear models for the marginal distribution for each outcome. We explain how to set the prior distribution and we use Markov chain Monte Carlo methods to calculate the posterior distribution. We propose two types of expanded models to diagnose whether each outcome indeed has predictor effects common with the other outcomes, and whether a particular predictor is commonly predictive for all outcomes. We determine a final model based on the diagnostic models. The method is applied to a study yielding multiple psychometric outcomes of mixed type measured in young people living with human immunodeficiency virus.
我们提出了一个贝叶斯多变量模型，其中协变量的单一线性组合可以同时预测多个结果。单一的线性组合是沿着危重病患者的 Apache 或 Charlson 指数评分、癌症患者的 Karnofsky 或 Eastern Cooperative Oncology Group 评分或心脏病患者的 Euro 评分的思路得出的得分，可用于预测多个结果。结果可以是离散的或连续的，我们为每个结果的边缘分布使用广义线性模型的组合。我们解释了如何设置先验分布，并使用马尔可夫链蒙特卡罗方法来计算后验分布。我们提出了两种扩展模型来诊断每个结果是否确实与其他结果具有共同的预测效果，以及特定的预测因子是否对所有结果都具有共同的预测作用。我们根据诊断模型确定最终模型。该方法应用于一项研究，该研究产生了年轻人携带人类免疫缺陷病毒时的多种混合类型的心理计量学结果。
