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

Source: R/lcmm-package.R
lcmm-package.Rd

Functions for the estimation of latent class mixed models (LCMM), joint latent class mixed models for longitudinal and survival data (JLCM) and latent process mixed models (with or without latent classes of trajectory) for univariate and multivariate longitudinal outcomes of different types including curvilinear and ordinal outcomes. 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:2.1.0
Date:2023-10-06
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 (ordinal/binary) longitudinal outcomes using lcmm function,

  • mixed models (with and without latent classes) for multivariate longitudinal outcomes of different nature using multlcmm function (this includes a longitudinal IRT model for homogeneous and heterogeneous data),

  • 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,

  • joint latent class mixed models for multivariate longitudinal outcomes and a right-censored (potentially left-truncated and of multiple causes) time-to-event using mpjlcmm function.

Please report any bug or comment regarding the package for future updates VIA GITHUB ONLY.

References

Proust-Lima C, Philipps V, Liquet B (2017). Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm. Journal of Statistical Software, 78(2), 1-56. doi:10.18637/jss.v078.i02

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

Proust-Lima, Philipps, Perrot, Blanchin, Sebille (2021). Modeling repeated self-reported outcome data: a continuous-time longitudinal Item Response Theory model. arXiv:210913064. http://arxiv.org/abs/2109.13064

Proust-Lima, Dartigues, Jacqmin-Gadda (2016). Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach. Stat Med;35(3):382-98

Proust-Lima, Philipps, Dartigues, Bennett, Glymour, Jacqmin-Gadda, et al (2019). Are latent variable models preferable to composite score approaches when assessing risk factors of change? Evaluation of type-I error and statistical power in longitudinal cognitive studies. Stat Methods Med Res;28(7):1942-57

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

Author

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

cecile.proust-lima@inserm.fr