Estimation of extended mixed models using latent classes and latent processes.

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Description

Functions for the estimation of latent class mixed models (LCMM), joint latent class mixed models (JLCM) and mixed models for curvilinear and ordinal univariate and multivariate longitudinal outcomes (with or without latent classes of trajectory). All the models are estimated in a maximum likelihood framework using an iterative algorithm. The package also provides various post fit functions.

Details

Package: lcmm
Type: Package
Version: 1.7.5
Date: 2016-03-15
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,

  • latent class mixed models for other quantitative, bounded quantitative (curvilinear) and discrete longitudinal outcomes using lcmm function,

  • latent class mixed models for multivariate (possibly curvilinear) longitudinal outcomes using multlcmm function,

  • joint latent class mixed models for a Gaussian (or curvilinear) longitudinal outcome and a right-censored (potentially left-truncated and of multiple causes) time-to-event using Jointlcmm function.

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

Author(s)

Cecile Proust-Lima, Viviane Philipps, Amadou Diakite and Benoit Liquet

cecile.proust-lima@inserm.fr

References

Proust-Lima C, Philipps V, Liquet B (2015). Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: the R package lcmm, Arxiv. http://arxiv.org/abs/1503.00890

Commenges, Liquet and Proust-Lima (2012). Choice of prognostic estimators in joint models by estimating differences of expected conditional Kullback-Leibler risks. Biometrics 68(2), 380-7.

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, Jacqmin-Gadda (2014). Joint latent class models for longitudinal and time-to-event data: a review. Statistical Methods in Medical Research 23, 74-90.

Proust-Lima, Amievan Jacqmin-Gadda (2013). Analysis of multivariate mixed longitudinal data: A flexible latent process approach. Br J Math Stat Psychol 66(3), 470-87.

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

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