He Kevin, Kalbfleisch Jack D, Li Yijiang, Li Yi
Department of Biostatistics, University of Michigan, 1420 Washington Hts., Ann Arbor, MI, 48109-2029, USA,
Lifetime Data Anal. 2013 Oct;19(4):490-512. doi: 10.1007/s10985-013-9264-6. Epub 2013 May 26.
Motivated by the national evaluation of readmission rates among kidney dialysis facilities in the United States, we evaluate the impact of including discharging hospitals on the estimation of facility-level standardized readmission ratios (SRRs). The estimation of SRRs consists of two steps. First, we model the dependence of readmission events on facilities and patient-level characteristics, with or without an adjustment for discharging hospitals. Second, using results from the models, standardization is achieved by computing the ratio of the number of observed events to the number of expected events assuming a population norm and given the case-mix in that facility. A challenging aspect of our motivating example is that the number of parameters is very large and estimation of high-dimensional parameters is troublesome. To solve this problem, we propose a structured Newton-Raphson algorithm for a logistic fixed effects model and an approximate EM algorithm for the logistic mixed effects model. We consider a re-sampling and simulation technique to obtain p-values for the proposed measures. Finally, our method of identifying outlier facilities involves converting the observed p-values to Z-statistics and using the empirical null distribution, which accounts for overdispersion in the data. The finite-sample properties of proposed measures are examined through simulation studies. The methods developed are applied to national dialysis data. It is our great pleasure to present this paper in honor of Ross Prentice, who has been instrumental in the development of modern methods of modeling and analyzing life history and failure time data, and in the inventive applications of these methods to important national data problem.
受美国肾脏透析机构再入院率全国评估的推动，我们评估了纳入出院医院对机构层面标准化再入院率（SRR）估计的影响。SRR的估计包括两个步骤。首先，我们对再入院事件与机构和患者层面特征之间的依赖性进行建模，无论是否对出院医院进行调整。其次，利用模型结果，通过计算观察到的事件数与假设总体规范并给定该机构病例组合情况下预期事件数的比率来实现标准化。我们激励示例中一个具有挑战性的方面是参数数量非常大，高维参数的估计很麻烦。为了解决这个问题，我们针对逻辑固定效应模型提出了一种结构化牛顿 - 拉夫森算法，针对逻辑混合效应模型提出了一种近似期望最大化（EM）算法。我们考虑一种重采样和模拟技术来获得所提出度量的p值。最后，我们识别异常值机构的方法包括将观察到的p值转换为Z统计量，并使用经验零分布，该分布考虑了数据中的过度离散。通过模拟研究检验了所提出度量的有限样本性质。所开发的方法应用于全国透析数据。我们非常荣幸地提交本文以纪念罗斯·普伦蒂斯，他在现代生命历程和失效时间数据建模与分析方法的发展以及将这些方法创造性地应用于重要的国家数据问题方面发挥了重要作用。
