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Regression analysis with clustered data

B I Graubard1, E L Korn

  • 1National Cancer Institute, Bethesda, Maryland 20892.

Statistics in Medicine
|March 15, 1994
PubMed
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This study examines clustered data analysis, comparing population average and cluster-specific models for estimating causal effects. It provides guidance on model selection and robust standard error estimation for treatment parameters.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survey Methodology

Background:

  • Clustered data are prevalent in various research designs, including repeated measures, household surveys, and community trials.
  • Standard analytical approaches for clustered data involve population average and cluster-specific models.

Purpose of the Study:

  • To discuss the appropriateness of population average and cluster-specific models for estimating causal effects with clustered data.
  • To explore conditions for agreement between these two modeling approaches.
  • To present methods for robust standard error estimation using survey sampling techniques.

Main Methods:

  • Comparative analysis of population average and cluster-specific models.
  • Discussion of conditions for model selection and agreement.

Related Experiment Videos

  • Application of survey sampling methods for standard error estimation.

    • Main Results:

    • Identifies specific conditions under which population average or cluster-specific models are suitable for causal inference.
    • Highlights scenarios where the two model types yield consistent results.
    • Demonstrates the utility of survey sampling methods for reliable estimation of treatment effect standard errors.

    Conclusions:

    • The choice between population average and cluster-specific models depends on the research question and data structure.
    • Understanding model agreement is crucial for valid causal effect estimation.
    • Robust standard error estimation is essential for accurate inference in clustered data analyses.