Department of Anesthesia, The University of Iowa, Iowa City, IA, USA.
BMC Med Res Methodol. 2013 Jan 16;13:5. doi: 10.1186/1471-2288-13-5.
To quantify the variability among centers and to identify centers whose performance are potentially outside of normal variability in the primary outcome and to propose a guideline that they are outliers.
Novel statistical methodology using a Bayesian hierarchical model is used. Bayesian methods for estimation and outlier detection are applied assuming an additive random center effect on the log odds of response: centers are similar but different (exchangeable). The Intraoperative Hypothermia for Aneurysm Surgery Trial (IHAST) is used as an example. Analyses were adjusted for treatment, age, gender, aneurysm location, World Federation of Neurological Surgeons scale, Fisher score and baseline NIH stroke scale scores. Adjustments for differences in center characteristics were also examined. Graphical and numerical summaries of the between-center standard deviation (sd) and variability, as well as the identification of potential outliers are implemented.
In the IHAST, the center-to-center variation in the log odds of favorable outcome at each center is consistent with a normal distribution with posterior sd of 0.538 (95% credible interval: 0.397 to 0.726) after adjusting for the effects of important covariates. Outcome differences among centers show no outlying centers. Four potential outlying centers were identified but did not meet the proposed guideline for declaring them as outlying. Center characteristics (number of subjects enrolled from the center, geographical location, learning over time, nitrous oxide, and temporary clipping use) did not predict outcome, but subject and disease characteristics did.
Bayesian hierarchical methods allow for determination of whether outcomes from a specific center differ from others and whether specific clinical practices predict outcome, even when some centers/subgroups have relatively small sample sizes. In the IHAST no outlying centers were found. The estimated variability between centers was moderately large.
量化各中心之间的变异性，并确定其绩效可能超出主要结局正常变异性的中心，并提出将其视为异常值的指导方针。
使用贝叶斯层次模型的新统计方法。应用贝叶斯方法进行估计和异常值检测，假设反应对数几率的中心效应是可加的随机效应：中心相似但不同（可交换）。以颅内动脉瘤手术术中低温试验（IHAST）为例。分析调整了治疗、年龄、性别、动脉瘤位置、世界神经外科学会分级、Fisher 评分和基线 NIH 卒中量表评分。还检查了对中心特征差异的调整。实现了中心间标准差（sd）和变异性的图形和数值摘要，以及潜在异常值的识别。
在 IHAST 中，每个中心的有利结局对数几率的中心间差异与正态分布一致，在调整了重要协变量的影响后，后验 sd 为 0.538（95%可信区间：0.397 至 0.726）。中心间的结局差异没有异常中心。确定了四个潜在的异常中心，但不符合提出的宣布为异常中心的指导方针。中心特征（中心招募的受试者数量、地理位置、随时间学习、氧化亚氮和临时夹闭的使用）不能预测结局，但受试者和疾病特征可以预测结局。
贝叶斯层次方法可用于确定特定中心的结果是否与其他中心不同，以及特定的临床实践是否可预测结局，即使某些中心/亚组的样本量相对较小。在 IHAST 中未发现异常中心。中心间的估计变异性较大。
