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A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers
| Content Provider | Scilit |
|---|---|
| Author | Li, Haocheng Zhang, Yukun Carroll, Raymond J. Keadle, Sarah Kozey Sampson, Joshua N. Matthews, Charles E. |
| Copyright Year | 2017 |
| Description | Journal: Statistics in medicine 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. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5656438/pdf |
| Ending Page | 4040 |
| Page Count | 13 |
| Starting Page | 4028 |
| e-ISSN | 10970258 |
| DOI | 10.1002/sim.7401 |
| Journal | Statistics in medicine |
| Issue Number | 25 |
| Volume Number | 36 |
| Language | English |
| Publisher | Wiley-Blackwell |
| Publisher Date | 2017-08-07 |
| Access Restriction | Open |
| Subject Keyword | Journal: Statistics in medicine Mathematical Social Sciences Statistics and Probability Longitudinal Data Mixed Effects Model Multivariate Longitudinal Data Penalized Quasi-likelihood |
| Content Type | Text |
| Resource Type | Article |
