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Hot Deck Multiple Imputation for Handling Missing Accelerometer Data.

Nicole M Butera1, Siying Li1, Kelly R Evenson2

  • 1Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill.

Statistics in Biosciences
|August 27, 2019
PubMed
Summary
This summary is machine-generated.

A new flexible hot deck multiple imputation (MI) method accurately handles missing accelerometer data from non-wear periods. This approach improves physical activity and sedentary behavior research by reducing bias and enhancing confidence interval coverage.

Keywords:
accelerometerhigh-dimensional datahot deckmissing datamultiple imputationphysical activity

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Area of Science:

  • Biomedical Engineering
  • Epidemiology
  • Data Science

Background:

  • Missing data due to non-wear is a significant challenge in accelerometer studies.
  • Existing methods for handling missing accelerometry data are often ad-hoc or rely on restrictive assumptions.
  • Accelerometer data are high-dimensional, episodic, and skewed time-series, complicating imputation.

Purpose of the Study:

  • To develop and evaluate a flexible hot deck multiple imputation (MI) procedure for addressing missing accelerometry data.
  • To compare the performance of hot deck MI against standard available case (AC) and complete case (CC) analyses.
  • To assess imputation performance for both 24-hour and daytime-only accelerometry data.

Main Methods:

  • Developed a hot deck MI procedure where missing segments are replaced by observed segments from "donor pools".
  • Donor pool selection and imputation weights were based on non-wear and accelerometer-derived variables.
  • Compared hot deck MI, AC, and CC analyses using a simulation study with 2,550 women's accelerometry data.

Main Results:

  • Hot deck MI demonstrated less bias and better 95% confidence interval (CI) coverage than AC and CC for 24-hour data.
  • For daytime-only data, MI showed less bias and better 95% CI coverage compared to AC.
  • CC analysis for daytime data yielded similar bias and 95% CI coverage but with longer CIs than MI.

Conclusions:

  • Flexible hot deck MI is a robust method for imputing missing accelerometry data, outperforming traditional AC and CC methods.
  • This imputation strategy enhances the accuracy and reliability of physical activity and sedentary behavior estimates derived from accelerometers.
  • The findings support the adoption of hot deck MI for improving the analysis of accelerometer data, particularly when dealing with significant missingness.