title: Evaluating health care performance: strengths and limitations of multilevel analysis.
abstract: 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.
authors.affiliation: Office of Epidemiology and Biostatistics, Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, TX 77555-1148, USA. altan@utmb.edu
authors.forename: Alai
authors.initials: A
authors.lastname: Tan
authors.forename: Jean L
authors.initials: JL
authors.lastname: Freeman
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Freeman
mesh_terms: D019985:Benchmarking / Q000379:methods*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008327:Mammography / Q000379:methods*; D015999:Multivariate Analysis; D015588:Observer Variation; D011785:Quality Assurance, Health Care / Q000379:methods*