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Activity Number: 443 - Making an Impact on Physical Activity and Sleep Research by Developing New Statistical Methods
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Korean International Statistical Society
Abstract #300168
Title: Sample Integrity in Physical Activity Experiments: R 'Accelmissing'
Author(s): Jung Ae Lee*
Companies: University of Arkansas
Keywords: accelmissing; physical activity; accelerometer; missing data

An accelerometer, a wearable motion sensor on the hip or wrist, is becoming a popular tool in clinical and epidemiological studies for measuring the physical activity. Such data provide a series of activity counts at every minute or even more often and displays a person’s activity pattern throughout a day. Unfortunately, the collected data can include irregular missing intervals because of noncompliance of participants and therefore make the statistical analysis difficult. The purpose of this study is to develop a novel imputation method to handle the multivariate count data, motivated by the accelerometer data structure. We specify the predictive distribution of the missing data with a mixture of zero-inflated Poisson and Log-normal distribution, which is shown to be effective to deal with the minute-by-minute autocorrelation as well as under- and over-dispersion of count data. The imputation applies the principles of multiple imputation using a fully conditional specification with the chained algorithm. To facilitate the practical use of this method, we provide an R package accelmissing. Our method is demonstrated using National Health and Nutrition Examination Survey data.

Authors who are presenting talks have a * after their name.

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