Evaluating health care performance: Strengths and limitations of multilevel analysis

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

    Research output: Contribution to journalArticlepeer-review

    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

    Fingerprint

    Dive into the research topics of 'Evaluating health care performance: Strengths and limitations of multilevel analysis'. Together they form a unique fingerprint.

    Cite this