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Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on

Anurika Priyanjali De Silva1, Margarita Moreno-Betancur2,3,4, Alysha Madhu De Livera2

  • 1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia. anurikad@student.unimelb.edu.au.

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Summary

Accounting for restrictions in longitudinal data imputation reduces bias. Fully conditional specification with predictive mean matching is recommended for accurate analysis of categorical variables with missing values.

Keywords:
Fully conditional specificationLongitudinal categorical dataMissing dataMultiple imputationMultivariate normal imputationRestricted transitions

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal categorical variables have inherent transition restrictions (e.g., never-smoker to ex-smoker).
  • Missing values in these variables are common, but guidance on handling restrictions during multiple imputation is limited.
  • Ignoring restrictions during imputation can lead to biologically implausible transitions and biased results.

Purpose of the Study:

  • To evaluate multiple imputation methods for longitudinal categorical variables with transition restrictions.
  • To compare the performance of various imputation techniques, including those that accommodate restrictions, using a simulation study.
  • To identify the most effective imputation strategy for analyzing restricted longitudinal data.

Main Methods:

  • A simulation study was designed using data from the Longitudinal Study of Australian Children.
  • Maternal smoking data (a restricted longitudinal categorical variable) was manipulated to be missing completely at random or missing at random.
  • Multiple imputation methods were compared, including fully conditional specification (FCS) and multivariate normal imputation (MVNI), with and without accounting for restrictions via a semi-deterministic procedure.

Main Results:

  • Multiple imputation methods that accounted for restrictions demonstrated reduced bias compared to those that ignored them.
  • Fully conditional specification with predictive mean matching (FCS-PMM) showed the best performance.
  • Multivariate normal imputation methods produced biased estimates when restrictions were not accommodated, but bias was reduced when restrictions were applied.

Conclusions:

  • For longitudinal categorical variables with transition restrictions, it is recommended to apply these restrictions during the imputation stage.
  • Fully conditional specification with predictive mean matching (FCS-PMM) is a robust method for imputing such data.
  • Accounting for restrictions improves the accuracy and plausibility of results in longitudinal data analysis.