Institut de Santé Publique,
d'Épidémiologie et de Développement
 

Centre Inserm U897
Equipe Biostatistique

LCMM

A R package for the estimation of extended mixed models using latent classes and latent processes .

Presentation

The R package LCMM fits a variety of statistical models derived from the mixed model theory. This includes:

  • standard linear mixed models (hlme function)
  • latent class linear mixed models that extend the linear mixed models to the study of heterogeneous populations (hlme function)
  • latent process mixed models for a single longitudinal outcome (possiblycurvilinear and/or ordinal) and their extension to the study of heterogeneous populations (lcmm function)
  • latent process mixed models for multivariate longitudinal outcomes (possiblycurvilinear and/or ordinal) and their extension to the study of heterogeneous populations (multlcmm function)
  • joint latent class mixed models that extend the linear mixed model (and the latent process mixed model) to the simultaneous modelling of a right-censored possibly left-truncated time-to-event (Jointlcmm function). This includes also multi-cause time-to-event through competing risks, and derived dynamic individual predictions.
  • All the functions of the package are described in detail in the companion paper (on arxiv).


    Download

    http://cran.r-project.org/web/packages/lcmm/index.html


    References

    Commenges, D., Proust-Lima, C., Samieri, C., & Liquet, B. (2014).
    A universal approximate cross-validation criterion for regular risk functions.
    /International journal of biostatistics/, /in press/.

    Commenges, D., Liquet, B., & Proust-Lima, C. (2012).
    Choice of prognostic estimators in joint models by estimating differences of expected conditional Kullback-Leibler risks.
    /Biometrics/, /68/(2), 380‑387. http://doi.org/10.1111/j.1541-0420.2012.01753.x

    Jacqmin-Gadda, H., Proust-Lima, C., Taylor, J. M. G., & Commenges, D. (2010).
    Score test for conditional independence between longitudinal outcome and time to event given the classes in the joint latent class model.
    /Biometrics/, /66/(1), 11‑19. http://doi.org/10.1111/j.1541-0420.2009.01234.x

    Proust, C., & Jacqmin-Gadda, H. (2005).
    Estimation of linear mixed models with a mixture of distribution for the random effects.
    /Computer Methods and Programs in Biomedicine/, /78/(2), 165‑173. http://doi.org/10.1016/j.cmpb.2004.12.004

    Proust, C., Jacqmin-Gadda, H., Taylor, J. M. G., Ganiayre, J., & Commenges, D. (2006).
    A nonlinear model with latent process for cognitive evolution using multivariate longitudinal data.
    /Biometrics/, /62/(4), 1014‑1024. http://doi.org/10.1111/j.1541-0420.2006.00573.x

    Proust-Lima, C., Amieva, H., & Jacqmin-Gadda, H. (2013).
    Analysis of multivariate mixed longitudinal data: a flexible latent process approach.
    /The British Journal of Mathematical and Statistical Psychology/, /66/(3), 470‑487. http://doi.org/10.1111/bmsp.12000

    Proust-Lima, C., Dartigues, J.-F., & Jacqmin-Gadda, H. (2011).
    Misuse of the linear mixed model when evaluating risk factors of cognitive decline.
    /American Journal of Epidemiology/, /174/(9), 1077‑1088. http://doi.org/10.1093/aje/kwr243

    Proust-Lima, C., Philipps, V., & Liquet, B. (2015).
    Estimation of extended mixed models using latent classes and latent processes: the R package lcmm.
    /arXiv:1503.00890 [stat]/. http://arxiv.org/abs/1503.00890

    Proust-Lima, C., Séne, M., Taylor, J. M., & Jacqmin-Gadda, H. (2014).
    Joint latent class models for longitudinal and time-to-event data: A review.
    /Statistical Methods in Medical Research/, /23/(1), 74‑90. http://doi.org/10.1177/0962280212445839

    Proust-Lima, C., & Taylor, J. M. G. (2009).
    Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach.
    /Biostatistics (Oxford, England)/, /10/(3), 535‑549. http://doi.org/10.1093/biostatistics/kxp009

    Authors

    Cécile Proust-Lima
    Benoit Liquet
    Amadou Diakité
    Viviane Philipps
    Lionelle Nkam

    Inserm U897
    146 rue Léo Saignat
    33076 Bordeaux Cedex
    France

    Contact

    E-mail: cecile.proust-lima@inserm.fr.
    We are interested in feed-back but can not guarantee support.

    Licence

    This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
    This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.


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