期刊
STATISTICAL METHODS IN MEDICAL RESEARCH
卷 27, 期 2, 页码 490-506出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280216633248
关键词
Accelerometer; physical activity; missing count data; multiple imputation; zero-inflated model; Poisson log-normal
类别
资金
- National Cancer Institute at the National Institutes of Health through the Transdisciplinary Research on Energetics and Cancer Center at Washinton Univeristy in St. Louis [U54 CA155496]
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 more challenging. 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 is performed at the minute level and follows 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 2003-2004 National Health and Nutrition Examination Survey data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据