Hot Deck Multiple Imputation for Handling Missing Accelerometer Data

Nicole M. Butera (), Siying Li, Kelly R. Evenson, Chongzhi Di, David M. Buchner, Michael J. LaMonte, Andrea Z. LaCroix and Amy Herring
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Nicole M. Butera: University of North Carolina at Chapel Hill
Siying Li: University of North Carolina at Chapel Hill
Kelly R. Evenson: University of North Carolina at Chapel Hill
Chongzhi Di: Fred Hutchinson Cancer Research Center
David M. Buchner: University of Illinois at Urbana-Champaign
Michael J. LaMonte: University at Buffalo
Andrea Z. LaCroix: University of California, San Diego
Amy Herring: Duke University

Statistics in Biosciences, 2019, vol. 11, issue 2, No 11, 422-448

Abstract: Abstract Missing data due to non-wear are common in accelerometer studies measuring physical activity and sedentary behavior. Accelerometer outputs are high-dimensional time-series data that are episodic and often highly skewed, presenting unique challenges for handling missing data. Common methods for missing accelerometry either are ad-hoc, require restrictive parametric assumptions, or do not appropriately impute bouts. This study developed a flexible hot-deck multiple imputation (MI; i.e., “replacing” missing data with observed values) procedure to handle missing accelerometry. For each missing segment of accelerometry, “donor pools” contained observed segments from either the same or different participants, and ten imputed segments were randomly drawn from the donor pool according to selection weights, where the donor pool and selection weight depended on variables associated with non-wear and/or accelerometer-based measures. A simulation study of 2550 women compared hot deck MI to two standard methods in the field: available case (AC) analysis (i.e., analyzing all observed accelerometry with no restriction on wear time or number of days) and complete case (CC) analysis (i.e., analyzing only participants that wore the accelerometer for ≥ 10 h for 4–7 days). This was repeated using accelerometry from the entire 24-h day and daytime (10am–8pm) only, and data were missing at random. For the entire 24-h day, MI produced less bias and better 95% confidence interval (CI) coverage than AC and CC. For the daytime only, MI produced less bias and better 95% CI coverage than AC; CC produced similar bias and 95% CI coverage, but longer 95% CIs than MI.

Keywords: Accelerometer; Multiple imputation; Missing data; High-dimensional data; Physical activity; Hot deck (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s12561-018-9225-4

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