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Applying machine learning-based multiple imputation methods to nonparametric multiple comparisons in longitudinal

Tuncay Yanarateş1, Erdem Karabulut1

  • 1Department of Biostatistics, School of Medicine, Hacettepe University, Ankara, Turkey.

Journal of Biopharmaceutical Statistics
|December 21, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning imputation methods, including MICE-CART and MICE-RF, are superior for handling missing data in dependent samples for nonparametric multiple comparisons. These methods outperform traditional listwise deletion, especially in smaller sample sizes.

Keywords:
Dependent samplesMICEmachine learningmissing at randommissing completely at randommissing data

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Dependent samples, where repeated measurements are taken on the same subjects, are common in research.
  • Missing data in k-dependent samples can complicate analysis, necessitating specialized methods like the Skillings-Mack test for non-normally distributed data.
  • Nonparametric multiple comparisons are crucial when significant group differences are detected in such datasets.

Purpose of the Study:

  • To propose and evaluate innovative methods for nonparametric multiple comparisons of incomplete k-dependent samples with non-normally distributed data.
  • To compare the performance of machine learning-based imputation methods against traditional imputation and deletion techniques.
  • To assess the effectiveness of these methods under various missing data mechanisms, correlation coefficients, sample sizes, and missingness percentages.

Main Methods:

  • Simulation study comparing four methods: Multiple Imputations by Chained Equations utilizing Classification and Regression Trees (MICE-CART), Multiple Imputations by Chained Equations using Random Forest (MICE-RF), random hot deck imputation, and listwise deletion.
  • Evaluation of methods across different missing data mechanisms, correlation coefficients, sample sizes (small and moderate), and missing data percentages (e.g., 10%, 20%, 30%).
  • Application of proposed methods to a longitudinal dentistry clinical trial to demonstrate practical utility.

Main Results:

  • Listwise deletion was found to be inferior to all other imputation methods.
  • MICE-CART and MICE-RF demonstrated superior performance, maintaining well-controlled type 1 error rates, particularly in moderate and small sample sizes.
  • Machine learning-based multiple imputation methods proved effective for nonparametric multiple comparisons with incomplete k-dependent samples.

Conclusions:

  • Machine learning-based multiple imputation methods (MICE-CART, MICE-RF) are recommended for nonparametric multiple comparisons of k-dependent samples with missing observations.
  • These advanced imputation techniques offer a robust solution for handling missing data in complex dependent sample designs.
  • The study validates the utility of machine learning for improving statistical analyses in the presence of missing data, as demonstrated in a clinical trial setting.