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Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes.

Stuart R Lipsitz1, Garrett M Fitzmaurice2, Roger D Weiss2

  • 1Division of General Internal Medicine, Brigham and Women's Hospital and Ariadne Labs, 1620 Tremont St. 3rd Floor, BC3 002D, Boston, MA, 02120-1613, USA. slipsitz@bwh.harvard.edu.

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Summary
This summary is machine-generated.

This study introduces multiple imputation (MI) methods to improve bias in longitudinal binary data analysis using generalized estimating equations (GEE) under missing at random (MAR) conditions. These novel MI techniques offer more accurate parameter estimation for marginal models with complex missing data patterns.

Keywords:
fully conditional specificationgeneralized estimating equationsmissing at randommissing completely at randommultivariate normal

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Missing Data Methods

Background:

  • Generalized estimating equations (GEE) provide consistent estimates for marginal models with data missing completely at random (MCAR).
  • Estimates from GEE may be inconsistent when longitudinal binary data are missing at random (MAR).
  • Non-monotone missing patterns in longitudinal binary responses pose challenges for standard statistical methods.

Purpose of the Study:

  • To develop and evaluate multiple imputation (MI) approaches for handling non-monotone missing longitudinal binary responses.
  • To minimize bias in parameter estimation for marginal models when data are missing at random (MAR).
  • To compare proposed MI methods against GEE without imputation in the presence of missing data.

Main Methods:

  • Proposed two MI approaches: one based on a multivariate normal distribution with outcome products, and another using fully conditional specification (FCS) with interaction terms.
  • Investigated the impact of not rounding imputed binary values in the multivariate normal approach to mitigate bias.
  • Explored the inclusion of pairwise outcome interactions within the FCS model to potentially reduce bias.

Main Results:

  • The proposed MI approaches demonstrated potential for bias reduction in longitudinal binary data with non-monotone missingness under MAR.
  • Comparison with GEE without imputation highlighted the advantages of the developed MI techniques in specific missing data scenarios.
  • Asymptotic bias analysis provided theoretical support for the effectiveness of the proposed imputation strategies.

Conclusions:

  • Multiple imputation offers a viable strategy to address bias in longitudinal binary data analysis when missing at random (MAR).
  • The proposed multivariate normal and FCS-based MI methods, particularly with interaction terms, can improve the accuracy of marginal model parameter estimates.
  • These methods are applicable to real-world longitudinal studies, such as clinical trials with repeated binary outcome measures.