Bayesian generalized varying coefficient models for longitudinal proportional data with errors-in-covariates |
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Authors: | Xiao-Feng Wang Bo Hu Bin Wang Kuangnan Fang |
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Affiliation: | 1. Department of Quantitative Health Sciences/Biostatistics Section, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA;2. Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA;3. Department of Statistics, Xiamen University, Xiamen, Fujian 361005, People's Republic of China |
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Abstract: | This paper is motivated from a neurophysiological study of muscle fatigue, in which biomedical researchers are interested in understanding the time-dependent relationships of handgrip force and electromyography measures. A varying coefficient model is appealing here to investigate the dynamic pattern in the longitudinal data. The response variable in the study is continuous but bounded on the standard unit interval (0, 1) over time, while the longitudinal covariates are contaminated with measurement errors. We propose a generalization of varying coefficient models for the longitudinal proportional data with errors-in-covariates. We describe two estimation methods with penalized splines, which are formalized under a Bayesian inferential perspective. The first method is an adaptation of the popular regression calibration approach. The second method is based on a joint likelihood under the hierarchical Bayesian model. A simulation study is conducted to evaluate the efficacy of the proposed methods under different scenarios. The analysis of the neurophysiological data is presented to demonstrate the use of the methods. |
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Keywords: | proportional data varying coefficient models errors-in-covariates MCMC penalized splines beta distribution |
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