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Hot Deck Multiple Imputation for Handling Missing Accelerometer Data

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nicole M. Butera & Siying Li & Kelly R. Evenson & Chongzhi Di & David M. Buchner & Michael J. LaMonte & Andrea Z. LaCroix & Amy Herring, 2019. "Hot Deck Multiple Imputation for Handling Missing Accelerometer Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 422-448, July.
    • Handle: RePEc:spr:stabio:v:11:y:2019:i:2:d:10.1007_s12561-018-9225-4
    DOI: 10.1007/s12561-018-9225-4
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    References listed on IDEAS

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    1. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
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