League Tables for Hospital Comparisons

Sharon Lise T. Normand; Arlene S. Ash; Stephen E. Fienberg; Thérèse A. Stukel; Jessica Utts; Thomas A. Louis

doi:10.1146/annurev-statistics-022513-115617

League Tables for Hospital Comparisons

Sharon Lise T. Normand, Arlene S. Ash, Stephen E. Fienberg, Thérèse A. Stukel, Jessica Utts, Thomas A. Louis

Bloomberg School of Public Health

Research output: Contribution to journal › Review article › peer-review

7 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	21-50
Number of pages	30
Journal	Annual Review of Statistics and Its Application
Volume	3
DOIs	https://doi.org/10.1146/annurev-statistics-022513-115617
State	Published - Jun 1 2016

Keywords

Bayesian inference
Hierarchical model
Low information
Observational data
Profiling
Risk adjustment

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1146/annurev-statistics-022513-115617

Cite this

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title = "League Tables for Hospital Comparisons",

abstract = "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.",

keywords = "Bayesian inference, Hierarchical model, Low information, Observational data, Profiling, Risk adjustment",

author = "Normand, {Sharon Lise T.} and Ash, {Arlene S.} and Fienberg, {Stephen E.} and Stukel, {Th{\'e}r{\`e}se A.} and Jessica Utts and Louis, {Thomas A.}",

note = "Funding Information: The authors thank Xihong Lin of theHarvard School of Public Health, who served as chair of the COPSS committee, and Steve Pierson and Keith Crank of the American Statistical Association for their efforts in establishing the COPSS committee and coordinating its work. Publisher Copyright: Copyright {\textcopyright}2016 by Annual Reviews. All rights reserved.",

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doi = "10.1146/annurev-statistics-022513-115617",

language = "English (US)",

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AU - Normand, Sharon Lise T.

AU - Ash, Arlene S.

AU - Fienberg, Stephen E.

AU - Stukel, Thérèse A.

AU - Utts, Jessica

AU - Louis, Thomas A.

N1 - Funding Information: The authors thank Xihong Lin of theHarvard School of Public Health, who served as chair of the COPSS committee, and Steve Pierson and Keith Crank of the American Statistical Association for their efforts in establishing the COPSS committee and coordinating its work. Publisher Copyright: Copyright ©2016 by Annual Reviews. All rights reserved.

PY - 2016/6/1

Y1 - 2016/6/1

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - Hierarchical model

KW - Low information

KW - Observational data

KW - Profiling

KW - Risk adjustment

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JO - Annual Review of Statistics and Its Application

JF - Annual Review of Statistics and Its Application

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League Tables for Hospital Comparisons

Abstract

Keywords

ASJC Scopus subject areas

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