lmeNBBayes: Compute the Personalized Activity Index Based on a Flexible Bayesian Negative Binomial Model
The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework.
- Yumi Kondo
- Date of publication
- 2015-02-21 01:31:16
- Yumi Kondo <email@example.com>
- GPL (>= 2)
- Estimate the random effect distribution of the flexible mixed...
- Compute the DIC given the output from 'lmeNBBayes'.
- Generate samples from the flexible mixed-effect negative...
- The main function to compute the point estimates and 95%...
- Generate posterior samples from a flexible mixed effect...
- Internal lmeNBBayes functions
Files in this package