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Hierarchical imputation of systematically and sporadically missing data: An approximate Bayesian approach using

Shahab Jolani1

  • 1Department of Methodology and Statistics, CAPHRI, Maastricht University, 6229, HA, Maastricht, The Netherlands.

Biometrical Journal. Biometrische Zeitschrift
|October 10, 2017
PubMed
Summary

Multiple imputation (MI) methods are enhanced for clustered health data with both systematic and sporadic missingness. This new approach ensures valid statistical inferences for complex hierarchical datasets.

Keywords:
conditional imputationmultilevel imputationmultiple imputation by chained equations (MICE)sequential regression imputation

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Area of Science:

  • Health and medical sciences
  • Biostatistics
  • Data science

Background:

  • Multiple imputation (MI) is crucial for handling missing data in health research.
  • Standard MI methods struggle with the hierarchical structure of clustered data (e.g., multicenter studies).
  • Simultaneously addressing systematic (cluster-level) and sporadic (individual-level) missing data is a significant challenge.

Purpose of the Study:

  • To develop a novel hierarchical imputation method for clustered data.
  • To simultaneously impute both systematically and sporadically missing data.
  • To provide valid statistical inferences for complex hierarchical datasets.

Main Methods:

  • A new class of hierarchical imputation approach based on chained equations methodology.
  • Utilizes a random effect imputation model, simplifying fully Bayesian techniques.
  • Directly obtains parameter draws within each step of the chained equations.

Main Results:

  • The proposed methodology demonstrates good statistical properties.
  • Simulation studies confirm low bias and accurate coverage rates for parameter estimates.
  • Theoretical arguments support the imputation approach's validity.

Conclusions:

  • The developed method effectively handles complex missing data patterns in clustered health data.
  • Offers a practical and statistically sound alternative for individual participant data meta-analysis and multicenter studies.
  • Facilitates more reliable inferences from complex health datasets.