Missing Step Count Data? Step Away From the Expectation–Maximization Algorithm

TY - JOUR

T1 - Missing Step Count Data? Step Away From the Expectation–Maximization Algorithm

AU - Tackney, Mia S.

AU - Stahl, Daniel

AU - Williamson, Elizabeth

AU - Carpenter, James

N1 - Funding Information: Tackney and Stahl were supported by Health Data Research UK, which is funded by the U.K. Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Govern-ment), Public Health Agency (Northern Ireland), and British Heart Foundation and Wellcome. Stahl received financial support by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King’s College London. Carpenter is supported by the Medical Research Council, grant numbers MC UU 12023/21 and MC UU 12023/ 29. Williamson is supported by Medical Research Council project grants MR/S01442X/1 and MR/R013489/1. The MOVE-IT trial was funded by the National Institute for Health Research Health Technology Assessment program (Project: 10/62/03). The views expressed are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. The MOVE-IT trial was reviewed and approved by the Dulwich Ethics Committee (reference: 12/LO/0917). Written informed consent was gained from all participants prior to undergoing screening in order to validate their eligibility to participate. Funding Information: Tackney and Stahl were supported by Health Data Research UK, which is funded by the U.K. Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), and British Heart Foundation and Wellcome. Stahl received financial support by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King’s College London. Carpenter is supported by the Medical Research Council, grant numbers MC UU 12023/21 and MC UU 12023/ 29. Williamson is supported by Medical Research Council project grants MR/S01442X/1 and MR/R013489/1. The MOVE-IT trial was funded by the National Institute for Health Research Health Technology Assessment program (Project: 10/62/03). The views expressed are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. The MOVE-IT trial was reviewed and approved by the Dulwich Ethics Committee (reference: 12/LO/0917). Written informed consent was gained from all participants prior to undergoing screening in order to validate their eligibility to participate. Publisher Copyright: © 2022 The Authors.

PY - 2022/12

Y1 - 2022/12

N2 - In studies that compare physical activity between groups of individuals, it is common for physical activity to be quantified by step count, which is measured by accelerometers or other wearable devices. Missing step count data often arise in these settings and can lead to bias or imprecision in the estimated effect if handled inappropriately. Replacing each missing value in accelerometer data with a single value using the Expectation–Maximization (EM) algorithm has been advocated in the literature, but it can lead to underestimation of variances and could seriously compromise study conclusions. We compare the performance in terms of bias and variance of two missing data methods, the EM algorithm and Multiple Imputation (MI), through a simulation study where data are generated from a parametric model to reflect characteristics of a trial on physical activity. We also conduct a reanalysis of the 2019 MOVE-IT trial. The EM algorithm leads to an underestimate of the variance of effects of interest, in both the simulation study and the reanalysis of the MOVE-IT trial. MI should be the preferred approach to handling missing data in accelerometer, which provides valid point and variance estimates.

AB - In studies that compare physical activity between groups of individuals, it is common for physical activity to be quantified by step count, which is measured by accelerometers or other wearable devices. Missing step count data often arise in these settings and can lead to bias or imprecision in the estimated effect if handled inappropriately. Replacing each missing value in accelerometer data with a single value using the Expectation–Maximization (EM) algorithm has been advocated in the literature, but it can lead to underestimation of variances and could seriously compromise study conclusions. We compare the performance in terms of bias and variance of two missing data methods, the EM algorithm and Multiple Imputation (MI), through a simulation study where data are generated from a parametric model to reflect characteristics of a trial on physical activity. We also conduct a reanalysis of the 2019 MOVE-IT trial. The EM algorithm leads to an underestimate of the variance of effects of interest, in both the simulation study and the reanalysis of the MOVE-IT trial. MI should be the preferred approach to handling missing data in accelerometer, which provides valid point and variance estimates.

KW - accelerometer

KW - missing data

KW - multiple imputation

KW - wearable devices

UR - http://www.scopus.com/inward/record.url?scp=85150389662&partnerID=8YFLogxK

U2 - 10.1123/jmpb.2022-0002

DO - 10.1123/jmpb.2022-0002

M3 - Article

AN - SCOPUS:85150389662

SN - 2575-6605

VL - 5

SP - 205

EP - 214

JO - Journal for the Measurement of Physical Behaviour

JF - Journal for the Measurement of Physical Behaviour

IS - 4

ER -