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An approximate generalized linear model with random effects for informative missing data

D Follmann1, M Wu

  • 1Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892, USA.

Biometrics
|March 1, 1995
PubMed
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This study introduces new models for handling missing data in longitudinal studies by linking response and missingness models with a shared random parameter. The approach approximates generalized linear models for improved analysis of complex data, including repeated binary outcomes.

Area of Science:

  • Biostatistics
  • Econometrics
  • Statistical Modeling

Background:

  • Longitudinal studies frequently encounter missing data, complicating analysis.
  • Existing models for Gaussian responses exist but are limited.
  • Handling missingness in generalized linear models requires advanced techniques.

Purpose of the Study:

  • To develop a flexible modeling framework for missing data in longitudinal studies.
  • To extend existing methods to generalized linear models for various data types.
  • To approximate complex models for practical application.

Main Methods:

  • Developed models linking primary response and missingness via a common random parameter.
  • Approximated generalized linear models by conditioning on missingness data.

Related Experiment Videos

  • Utilized mixed generalized linear models with heterogeneous random effects.

    • Main Results:

    • The proposed method provides an approximation for generalized linear models with missing data.
    • The approach is illustrated with an example.
    • Simulations assess the approximation's adequacy for repeated binary data.

    Conclusions:

    • The developed approximate models offer a viable approach for analyzing longitudinal data with missing values.
    • The methodology is adaptable to various response variable distributions.
    • Further validation is provided through simulation studies.