Title: Sample from the posterior of the negative binomial mixed-effect
regression model. The random effect is modeled with DP mixture
Description: This package contains a gibbs sampler to sample from the
posterior distribution of the negative binomial mixed-effect
regression model. For the distribution of subject-specific
random intercept, user can choose a beta distribution or an
infinite mixture of beta distributions. For the safety
monitoring, subject-specific conditional probability indices
can be computed. For details, see Kondo,Y. and Zhao, Y. A
Flexible Mixed Effect Negative Binomial Regression for
Detecting Abrupt Increase in MRI Lesion Counts in Multiple

Description: This package contains a gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. For the distribution of subject-specific random intercept, user can choose a beta distribution or an infinite mixture of beta distributions. For the safety monitoring, subject-specific conditional probability indices can be computed. For details, see Kondo,Y. and Zhao, Y. A Flexible Mixed Effect Negative Binomial Regression for Detecting Abrupt Increase in MRI Lesion Counts in Multiple Sclerosis Patients.

Description: This package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. For the distribution of subject-specific random intercept, user can choose a beta distribution or an infinite mixture of beta distributions. For the safety monitoring, subject-specific conditional probability indices can be computed.  For details, see Kondo,Y., Zhao, Y. and Petkau, J.A. A Flexible Mixed Effect Negative Binomial Regression for Detecting Abrupt Increase in MRI Lesion Counts in Multiple Sclerosis Patients.

Title: Compute the Personalized Activity Index Based on a Flexible
Description: 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.
Packaged: 2015-02-20 22:51:57 UTC; yumikondo