Premium
Evaluating Health Care Performance: Strengths and Limitations of Multilevel Analysis
Author(s) -
Tan Alai,
Freeman Jean L.,
Freeman Daniel H.
Publication year - 2007
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200610350
Subject(s) - estimator , multilevel model , sampling (signal processing) , profiling (computer programming) , health care , cluster (spacecraft) , cluster sampling , statistics , computer science , data mining , econometrics , mathematics , medicine , environmental health , population , filter (signal processing) , economics , computer vision , programming language , economic growth , operating system
An increasing number of health services researchers are using multilevel analysis for evaluating health care performance. This method has the distinct advantage of accounting for within‐provider correlation among patients. Alternatively, in a similar manner, estimators based on cluster sampling can also adjust for within‐provider correlation. Cluster sampling methods do not require assumptions about error distribution as multilevel analysis does. To our knowledge, no comparison has been made between multilevel analysis and cluster sampling estimators in evaluating health care performance using either a simulated or real dataset. In this paper, we compare the cluster sampling estimators to multilevel estimators in evaluating screening mammography performance using Medicare claims data. We also discuss the strengths and limitations of multilevel analysis in profiling health care providers with small caseloads. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)