Evaluating health care performance: Strengths and limitations of multilevel analysis

Alai Tan, Jean L. Freeman, Daniel H. Freeman

Research output: Contribution to journalArticle

8 Scopus citations

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 multi-level 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.

Original languageEnglish (US)
Pages (from-to)707-718
Number of pages12
JournalBiometrical Journal
Volume49
Issue number5
DOIs
StatePublished - Aug 2007

Keywords

  • Cluster sampling
  • Mammography
  • Medicare claims
  • Multilevel analysis
  • Provider profiling

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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