lcmm-package: Estimation of Latent Class Mixed Models and Joint Latent Class Mixed Models for quantitative, bounded quantitative, discrete and ordinal longitudinal outcomes and a right-censored left-truncated time-to-event.
Description
This package provides functions for the estimation of latent class mixed models (LCMM) and joint latent class mixed models using a maximum likelihood method.Details
ll{
Package: lcmm
Type: Package
Version: 1.4-1
Date: 2011-07-05
License: GPL (>=2.0)
LazyLoad: yes
}
The package includes for the moment the estimation of latent class mixed models for Gaussian longitudinal outcomes using hlme function, and for other quantitative, bounded quantitative and discrete longitudinal outcomes using lcmm function, and joint latent class mixed model for Gaussian longitudinal outcome and a time-to-event using Jointlcmm function.
Please report to the maintainer any bug or comment regarding the package for future updates.References
Lin, Turnbull, McCulloch and Slate (2002). Latent class models for
joint analysis of longitudinal biomarker and event process data: application to longitudinal
prostate-specific antigen readings and prostate cancer. Journal of the American Statistical
Association 97, 53-65.
Muthen and Shedden (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 55, 463-9
Proust and Jacqmin-Gadda (2005). Estimation of linear mixed models with a mixture of distribution for the random-effects. Comput Methods Programs Biomed 78:165-73
Proust, Jacqmin-Gadda, Taylor, Ganiayre, and Commenges (2006). A
nonlinear model with latent process for cognitive evolution using multivariate longitudinal
data. Biometrics 62, 1014-24.
Proust-Lima, Dartigues, and Jacqmin-Gadda (2011). Misuse of the linear mixed
model when evaluating risk factors of cognitive decline. Amer J Epidemiol - in press
Proust-Lima and Taylor (2009). Development and validation of a dynamic prognostic
tool for prostate cancer recurrence using repeated measures of post-treatment PSA: a joint
modelling approach. Biostatistics 10, 535-49.
Verbeke and Lesaffre (1996). A linear mixed-effects model with heterogeneity in
the random-effects population. Journal of the American Statistical Association 91, 217-21