A Bayesian hierarchical model for multi-level repeated ordinal data: Analysis of oral practice examinations in a large anaesthesiology training program

作     者：Tan, M Qu, YS Mascha, E Schubert, A 

作者机构：St Jude Childrens Res Hosp Dept Epidemiol & Biostat Memphis TN 38105 USA Cleveland Clin Fdn Dept Biostat & Epidemiol Cleveland OH 44195 USA Cleveland Clin Fdn Dept Gen Anesthesiol Cleveland OH 44195 USA 

出 版 物：《STATISTICS IN MEDICINE》 (医学统计学)

年 卷 期：1999年第18卷第15期

页      面：1983-1992页

核心收录：

学科分类：0710[理学-生物学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1001[医学-基础医学(可授医学、理学学位)] 0714[理学-统计学（可授理学、经济学学位）] 10[医学] 

基　　金：NCI NIH HHS [CA21765] Funding Source: Medline 

主　　题：麻醉学/教育 贝叶斯定理 教育考核/统计学和数值数据 实习医师和住院医师职务/标准 Markov链 模型 生物学 蒙特卡罗法 观察者偏差 人类 

摘      要：Oral practice examinations (OPEs) are used in many anaesthesiology programmes to familiarize anaesthesiology residents with the format of the oral examination administered by the American Board of Anesthesiology. The OPE outcome (final grade) consists of Definite Not Pass , Probable Not Pass , Probable Pass and Definite Pass . In our study to assess the validity of the OPE, residents took an average of two (ranging from one to six) OPEs, each of which was evaluated by two board certified anaesthesiologists randomly selected from a pool of 12. A key question of interest was to identify factors, for example, the length of training, didactic experience and other characteristics, that most influence OPE outcome. In addition, we were interested in assessing the reliability of the final grade, that is, the covariance parameters are of interest as well. However, estimating variance components in multi-level data with an unequal number of repeated ordinal outcomes presents several statistical challenges, such as how to estimate high dimensional random effects parameters, especially for ordinal outcomes. We propose a Bayesian hierarchical proportional odds model for data with such complexity. The flexibility of such a model allows us to make inference on the association of OPE outcomes with other factors and to estimate the variance components as well. Copyright (C) 1999 John Wiley & Sons, Ltd.