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Logistic regression vs. predictive mean matching for imputing binary covariates.

Peter C Austin1,2,3, Stef van Buuren4,5

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Statistical Methods in Medical Research
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PubMed
Summary
This summary is machine-generated.

Predictive mean matching offers a faster alternative to logistic regression for imputing missing binary data within Multivariate Imputation using Chained Equations (MICE) simulations, with comparable statistical performance.

Keywords:
Missing dataMonte Carlo simulationsmultiple imputation

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Area of Science:

  • Statistics
  • Computational Statistics
  • Data Science

Background:

  • Multivariate Imputation using Chained Equations (MICE) is a common method for handling missing data.
  • Parametric imputation (logistic regression) is standard for binary variables, but predictive mean matching is faster in R.
  • Limited research exists on predictive mean matching's statistical performance for binary variable imputation.

Purpose of the Study:

  • To compare the statistical performance of predictive mean matching against logistic regression for imputing missing binary variables.
  • To evaluate these methods under varying sample sizes and missing data prevalences.

Main Methods:

  • Monte Carlo simulations were employed to assess performance.
  • The analysis model of interest was a multivariable logistic regression.
  • Simulations varied sample sizes (N=250 to 10,000) and missing data prevalence (5% to 50%).

Main Results:

  • Predictive mean matching demonstrated statistical performance virtually identical to logistic regression for binary variable imputation.
  • This equivalence held across diverse sample sizes and missing data percentages.
  • Predictive mean matching significantly reduced computational time in simulations.

Conclusions:

  • Predictive mean matching is a statistically sound method for imputing missing binary variables.
  • It offers substantial computational efficiency gains for multiple imputation simulations.
  • Researchers can confidently use predictive mean matching for binary data imputation, especially when speed is a concern.