League Tables for Hospital Comparisons

被引：25
|
作者：
Normand, Sharon-Lise T. [1 ,2 ]
Ash, Arlene S. [3 ]
Fienberg, Stephen E. [4 ]
Stukel, Therese A. [5 ,6 ,7 ]
Utts, Jessica [8 ]
Louis, Thomas A. [9 ]
机构：
[1] Harvard Univ, Sch Med, Dept Hlth Care Policy, Boston, MA 02115 USA
[2] Harvard Univ, TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
[3] Univ Massachusetts, Sch Med, Dept Quantitat Hlth Sci, Worcester, MA 01605 USA
[4] Carnegie Mellon Univ, Dept Stat, Pittsburgh, PA 15213 USA
[5] Inst Clin Evaluat Sci, Toronto, ON M4N 3M5, Canada
[6] Univ Toronto, Inst Hlth Policy Management & Evaluat, Toronto, ON M5T 3M6, Canada
[7] Dartmouth Inst Hlth Policy & Clin Practice, Lebanon, NH 03766 USA
[8] Univ Calif Irvine, Dept Stat, Irvine, CA 92697 USA
[9] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Biostat, Baltimore, MD 21205 USA
来源：
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 3 | 2016年 / 3卷
关键词：
hierarchical model; observational data; profiling; Bayesian inference; risk adjustment; low information; VOLUME; MODEL; PERFORMANCE; REGRESSION; MORTALITY; OUTCOMES; 2-STAGE; CARE; SELECTION; CHECKING;
D O I：
10.1146/annurev-statistics-022513-115617
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
We review statistical methods for estimating and interpreting league tables used to infer hospital quality with a primary focus on methods for partitioning variation into two types: (a) that associated with within-hospital variation for a homogeneous group of patients and (b) that produced by between-hospital variation. We discuss the types of covariates included in the model, hierarchical and nonhierarchical logistic regression models for conducting inferences in a low-information context and their associated trade-offs, and the role of hospital volume. We use all-cause mortality rates for US hospitals to illustrate concepts and methods.
引用
收藏
页码：21 / 50
页数：30
相关论文
共 50 条
12345