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Related Experiment Videos

Software for hierarchical modeling of epidemiologic data

J S Witte1, S Greenland, L L Kim

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44109, USA.

Epidemiology (Cambridge, Mass.)
|September 8, 1998
PubMed
Summary

Hierarchical models offer stable estimates and handle complex data for epidemiology. This study provides SAS code to make these advanced statistical methods accessible to epidemiologists.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Conventional analytical approaches in epidemiology can yield unstable parameter estimates.
  • Existing statistical methods struggle with issues like multiple comparisons and multilevel data structures.
  • Hierarchical models offer a robust alternative for analyzing complex epidemiologic data.

Purpose of the Study:

  • To address the limited use of hierarchical models in epidemiology due to fitting difficulties.
  • To introduce accessible software and provide practical SAS code for applying hierarchical models to epidemiologic data.
  • To facilitate the adoption of hierarchical modeling techniques within the field of epidemiology.

Main Methods:

  • Review of existing software packages for fitting hierarchical models.

Related Experiment Videos

  • Development and presentation of SAS code tailored for epidemiologic data analysis using hierarchical models.
  • Demonstration of hierarchical modeling within a statistical framework suitable for multilevel data.

    • Main Results:

    • Hierarchical models provide more reasonable and stable parameter estimates compared to conventional methods.
    • The described approach effectively addresses challenges such as multiple comparisons.
    • The provided SAS code enables epidemiologists to implement hierarchical models using readily available software.

    Conclusions:

    • Hierarchical models are a valuable tool for enhancing the rigor and stability of epidemiologic research.
    • The accessibility of fitting these models has been improved through the provision of specific software guidance and SAS code.
    • Wider application of hierarchical models is anticipated in epidemiologic data analysis, leading to more robust findings.