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A comparison of multiple imputation methods for incomplete longitudinal binary data.

Yusuke Yamaguchi1, Toshihiro Misumi1, Kazushi Maruo2

  • 1a Japan-Asia Data Science , Development, Astellas Pharma Inc. , Tokyo , Japan.

Journal of Biopharmaceutical Statistics
|September 9, 2017
PubMed
Summary
This summary is machine-generated.

Multiple imputation methods are crucial for analyzing longitudinal binary data in clinical trials. Simulation results recommend specific multiple imputation techniques for unbiased treatment effect estimation, outperforming naive methods sensitive to missing data.

Keywords:
Longitudinal binary datamissing datamultiple imputation

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Longitudinal Data Analysis

Background:

  • Longitudinal binary data are common in clinical trials, necessitating robust statistical methods.
  • Multiple imputation (MI) is a key technique for handling missing data under the missing at random assumption.
  • Limited research compares the performance of various MI methods for longitudinal binary data, especially for period-based treatment effect evaluation.

Purpose of the Study:

  • To compare the performance of six multiple imputation methods in the SAS MI procedure for longitudinal binary data.
  • To assess treatment effects at a specific time point and over a period.
  • To identify reliable MI methods for unbiased and robust estimation of treatment effects in clinical research.

Main Methods:

  • Conducted an extensive simulation study.
  • Evaluated six multiple imputation methods available in SAS MI.
  • Assessed two primary endpoints: responder rates at a time point and over a period.
  • Compared MI methods against naive approaches like complete case analysis and single imputation.

Main Results:

  • Naive methods (complete case analysis, single imputation) showed high sensitivity to missing data.
  • Multiple imputation using a monotone method demonstrated robust performance.
  • Full conditional specification with a logistic regression imputation model also yielded unbiased estimations.
  • Recommended MI methods provide more reliable treatment effect estimates than naive approaches.

Conclusions:

  • The choice of multiple imputation method significantly impacts the validity of treatment effect estimations in longitudinal binary data.
  • Monotone and logistic regression-based full conditional specification imputation methods are recommended for clinical trials with missing longitudinal binary data.
  • These recommended methods offer unbiased and robust treatment effect estimates, crucial for accurate clinical evaluations.