A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers
- Publication Type:
- Journal Article
- Citation:
- Statistics in Medicine, 2017, 36 (25), pp. 4028 - 4040
- Issue Date:
- 2017-11-10
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Li_et_al-2017-Statistics_in_Medicine.pdf | Published Version | 989.92 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Copyright © 2017 John Wiley & Sons, Ltd. 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.
Please use this identifier to cite or link to this item: