A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers |
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| Authors: | Haocheng Li Yukun Zhang Raymond J. Carroll Sarah Kozey Keadle Joshua N. Sampson Charles E. Matthews |
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| Affiliation: | 1. Departments of Oncology and Community Health Sciences, University of Calgary, Calgary, Canada;2. Department of Oncology, University of Calgary, Calgary, Canada;3. Department of Statistics, Texas A&M University, College Station, USA;4. School of Mathematical and Physical Sciences, University of Technology Sydney, Broadway, Australia;5. Kinesiology Department, California Polytechnic State University, San Luis Obispo, USA;6. National Cancer Institute, Bethesda, USA |
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| Abstract: | A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi‐likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study. |
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| Keywords: | accelerometers longitudinal data mixed effects model multivariate longitudinal data penalized quasi‐likelihood |
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