Daniels Michael J, Normand Sharon-Lise T
Department of Statistics, University of Florida, Gainesville, 32611, USA.
Biostatistics. 2006 Jan;7(1):1-15. doi: 10.1093/biostatistics/kxi036. Epub 2005 May 25.
Monitoring health care quality involves combining continuous and discrete outcomes measured on subjects across health care units over time. This article describes a Bayesian approach to jointly modeling multilevel multidimensional continuous and discrete outcomes with serial dependence. The overall goal is to characterize trajectories of traits of each unit. Underlying normal regression models for each outcome are used and dependence among different outcomes is induced through latent variables. Serial dependence is accommodated through modeling the pairwise correlations of the latent variables. Methods are illustrated to assess trends in quality of health care units using continuous and discrete outcomes from a sample of adult veterans discharged from 1 of 22 Veterans Integrated Service Networks with a psychiatric diagnosis between 1993 and 1998.
监测医疗质量涉及将随时间在各医疗单位的受试者身上测量的连续和离散结果相结合。本文描述了一种贝叶斯方法，用于对具有序列依赖性的多层次多维度连续和离散结果进行联合建模。总体目标是刻画每个单位特征的轨迹。对每个结果使用基础的正态回归模型，并通过潜在变量诱导不同结果之间的依赖性。通过对潜在变量的成对相关性进行建模来处理序列依赖性。文中举例说明了如何使用1993年至1998年间从22个退伍军人综合服务网络中的1个出院且有精神疾病诊断的成年退伍军人样本的连续和离散结果，来评估医疗单位的质量趋势。
