AI Article Synopsis
- A mixed effect model is introduced to analyze different types of longitudinal data (continuous, proportion, count, and binary) by modeling variable associations with correlated random effects.
- The model employs quasi-likelihood approximations for nonlinear variables and is reformulated as a multivariate linear mixed model for effective estimation and inference.
- The methodology is demonstrated using physical activity data from a wearable accelerometer and validated through a simulation study.
Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5656438 | PMC |
| http://dx.doi.org/10.1002/sim.7401 | DOI Listing |
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