TY - JOUR
T1 - Missing Step Count Data? Step Away from the EM Algorithm
AU - Tackney, Mia S.
AU - Williamson, Elizabeth
AU - Stahl, Daniel
AU - Carpenter, James
PY - 2022/7/25
Y1 - 2022/7/25
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 is generated from a parametric model to reflect characteristics of a trial on physical activity, and a re-analysis 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 re-analysis of the MOVE-IT trial. Multiple Imputation 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 is generated from a parametric model to reflect characteristics of a trial on physical activity, and a re-analysis 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 re-analysis of the MOVE-IT trial. Multiple Imputation should be the preferred approach to handling missing data in accelerometer, which provides valid point and variance estimates.
M3 - Article
JO - Journal for the Measurement of Physical Behaviour
JF - Journal for the Measurement of Physical Behaviour
ER -