lcmm-package: Estimation of Mixed Models, Latent Class Mixed Models and Joint Latent Class Mixed Models for different types of outcomes (quantitative, bounded, curvilinear and ordinal outcomes).

Description

This package provides functions for the estimation of latent class mixed models (LCMM) and joint latent class mixed models (JLCM) using a maximum likelihood method. It also estimates mixed models for curvilinear and ordinal longitudinal outcomes (with or without latent classes of trajectories).

Arguments

Details

ll{ Package: lcmm Type: Package Version: 1.5.7 Date: 2012-07-24 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 (curvilinear) and discrete longitudinal outcomes using lcmm function, and joint latent class mixed models for a Gaussian longitudinal outcome and a right-censored (potentially left-truncated) time-to-event using Jointlcmm function.

Please report to the maintainer any bug or comment regarding the package for future updates.

References

Commenges, Liquet Proust-Lima (2012). Choice of prognostic estimators in joint models by estimating differences of expected conditional {K}ullback-{L}eibler risks. Biometrics - in press

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 174(9), 1077-88

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.

Proust-Lima, Sene, Taylor and Jacqmin-Gadda (2012). Joint latent class models of longitudinal and time-to-event data: a review. Statistical Methods in Medical Research - in press

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