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Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on

Molly Ehrig1, Garrett S Bullock1, Xiaoyan Iris Leng1

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JMIR Medical Informatics
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Summary
This summary is machine-generated.

The missing indicator method does not improve or reduce model performance or imputation accuracy in longitudinal data analysis. This method is neither beneficial nor detrimental when handling missing data in electronic health records for prediction models.

Keywords:
EHRclinical prediction modelelectronic health record dataelectronic health recordsfallsimputationlogistic regressionlongitudinal datamissing datamissing indicator methodolder adultsprediction modelprediction modelingsimulation study

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Area of Science:

  • Statistics
  • Biostatistics
  • Health Informatics

Background:

  • Missing data in electronic health records (EHRs) presents analytical challenges, including bias and reduced statistical power.
  • The missing indicator method, treating missingness as a category, is a simple approach for handling unknown covariate values.
  • Its utility in longitudinal data, where repeated measures can be leveraged, remains unclear compared to cross-sectional analyses.

Purpose of the Study:

  • To evaluate the missing indicator method's impact on model performance and imputation accuracy for longitudinal data.
  • To assess its effectiveness in a simulation study mimicking EHR-based clinical prediction models for falls in older adults.

Main Methods:

  • Simulated longitudinal binary outcomes using mixed-effects logistic regression.
  • Incorporated time-invariant and dynamic predictors, inducing missing data under random and non-random scenarios.
  • Evaluated model performance using Area Under the Receiver Operating Characteristic Curve (AUROC) and imputation quality via normalized root-mean-square error and percent falsely classified.

Main Results:

  • Model performance (AUROC) and imputation accuracy were similar with or without missing indicators, irrespective of the missing data mechanism (random or non-random).
  • Inclusion of missing indicators did not affect performance or imputation quality, even when the outcome was related to missingness.
  • Imputation methods showed comparable AUROC means to complete case analysis, though complete case analysis had greater variability.

Conclusions:

  • The missing indicator method neither enhances nor diminishes performance or imputation accuracy in longitudinal data modeling.
  • Further research is warranted to explore its utility in prediction modeling with longitudinal data, particularly in high-dimensional settings.