.png){kind=icon}
.png){kind=icon}
Return
Bayesian Multilevel Compositional Data Analysis: Introduction, Evaluation, and Application
.png){kind=icon}
OA
AI
F
S
D
J
DOI：10.1037/met0000750.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multilevel compositional data are data that are repeatedly measured or clustered within groups, and are nonnegative and sum to a constant value. These data arise in various settings, such as intensive, longitudinal studies using ecological momentary assessments and wearable devices. Examples include 24-hr sleep-wake behaviors, sleep architecture, and macronutrients. This article presents an innovative method for analyzing multilevel compositional data using Bayesian inference. We describe the theoretical details of the data and the models, and outline the steps necessary to implement this method. We introduce the R package multilevelcoda to facilitate the application of this method and illustrate using a real data example. An extensive parameter recovery simulation study verified the robust performance of the method. Across all conditions investigated in the simulation study, the fitted models had minimal convergence issues (convergence rate > 99%) and achieved excellent quality parameter estimates and inference, with an average bias of 0.00 (range = -0.09 to 0.05) and coverage of 0.95 (range = 0.93 to 0.97). We conclude the article with recommendations on the use of the Bayesian multilevel compositional data analysis. We hope to promote wider application of this method to gain novel and robust answers to scientific questions.
Keywords:
multilevel modeling
compositional data analysis
isotemporal substitution model
Bayesian inference
intensive longitudinal data
Journal
IF:
7.8
Papers:
1.3K
Citations:
2.1W
Organization
Cited Papers