The functions in this package implement the safety monitoring procedures proposed in the paper titled "Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., published in Journal of the American Statistical Association in 2013. 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 can be modelled with a gamma or log-normal distributions. One can also choose the semi-parametric option which does not assume any distribution for the random effect. These mixed-effect models could be useful beyond the application of the safety monitoring. The maximum likelihood methods are used to estimate the unknown fixed effect parameters of the model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNBBayes, which contains the functions to compute the Personalized Activity Index in Bayesian framework.

Author | Yinshan Zhao and Yumi Kondo (with contributions from Steven G. Johnson, Rudolf Schuerer and Brian Gough on the integration subroutines) |

Date of publication | 2015-02-02 22:40:23 |

Maintainer | Yumi Kondo <y.kondo@stat.ubc.ca> |

License | GPL (>= 2) |

Version | 1.3 |

**CP_se:** Compute a conditional probability of observing a set of...

**fitParaAR1:** Performs the maximum likelihood estimation for the negative...

**fitParaIND:** Performs the maximum likelihood estimation for the negative...

**fitSemiAR1:** Fit the semi-parametric negative binomial mixed-effect AR(1)...

**fitSemiIND:** Fit the semi-parametric negative binomial mixed-effect...

**index_batch:** The main function to compute the point estimates and 95%...

**jCP_ar1:** Compute a conditional probability of observing a set of...

**lmeNB:** Performs the maximum likelihood estimation for the negative...

**lmeNB-internal:** Internal lmeNB functions

**RElmeNB:** Calculate predicted values of E(Gi|Yi) given the estimates of...

**rNBME_R:** Simulate a dataset from the negative binomial mixed-effect...

lmeNB

lmeNB/src

lmeNB/src/ARlk.c

lmeNB/src/converged.h

lmeNB/src/vwrapper.h

lmeNB/src/cubature.h

lmeNB/NAMESPACE

lmeNB/R

lmeNB/R/oldCode.R
lmeNB/R/CPI.R
lmeNB/R/AR1models.R
lmeNB/R/INDmodels.R
lmeNB/R/Simulation.R
lmeNB/R/nbinomRE-internal.R
lmeNB/MD5

lmeNB/DESCRIPTION

lmeNB/man

lmeNB/man/fitSemiIND.Rd
lmeNB/man/index_batch.Rd
lmeNB/man/rNBME_R.Rd
lmeNB/man/fitParaAR1.Rd
lmeNB/man/fitParaIND.Rd
lmeNB/man/fitSemiAR1.Rd
lmeNB/man/jCP_ar1.Rd
lmeNB/man/CP_se.Rd
lmeNB/man/lmeNB.Rd
lmeNB/man/lmeNB-internal.Rd
lmeNB/man/RElmeNB.Rd
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