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Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm

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  • Proust-Lima, Cécile
  • Philipps, Viviane
  • Liquet, Benoit

Abstract

The R package lcmm provides a series of functions to estimate statistical models based on linear mixed model theory. It includes the estimation of mixed models and latent class mixed models for Gaussian longitudinal outcomes (hlme), curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear multivariate outcomes (multlcmm), as well as joint latent class mixed models (Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a time-to-event outcome that can be possibly left-truncated right-censored and defined in a competing setting. Maximum likelihood esimators are obtained using a modified Marquardt algorithm with strict convergence criteria based on the parameters and likelihood stability, and on the negativity of the second derivatives. The package also provides various post-fit functions including goodness-of-fit analyses, classification, plots, predicted trajectories, individual dynamic prediction of the event and predictive accuracy assessment. This paper constitutes a companion paper to the package by introducing each family of models, the estimation technique, some implementation details and giving examples through a dataset on cognitive aging.

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  • Proust-Lima, Cécile & Philipps, Viviane & Liquet, Benoit, 2017. "Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i02).
    • Handle: RePEc:jss:jstsof:v:078:i02
    DOI: http://hdl.handle.net/10.18637/jss.v078.i02
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    1. Lin H. & Turnbull B. W. & McCulloch C. E. & Slate E. H., 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, American Statistical Association, vol. 97, pages 53-65, March.
    2. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    3. Cécile Proust & Hélène Jacqmin-Gadda & Jeremy M. G. Taylor & Julien Ganiayre & Daniel Commenges, 2006. "A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1014-1024, December.
    4. Grün, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
    5. Hélène Jacqmin-Gadda & Cécile Proust-Lima & Jeremy M.G. Taylor & Daniel Commenges, 2010. "Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model," Biometrics, The International Biometric Society, vol. 66(1), pages 11-19, March.
    6. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    7. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    8. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
    9. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.
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    2. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    3. Jenny Rossen & Maria Hagströmer & Kristina Larsson & Unn-Britt Johansson & Philip von Rosen, 2022. "Physical Activity Patterns among Individuals with Prediabetes or Type 2 Diabetes across Two Years—A Longitudinal Latent Class Analysis," IJERPH, MDPI, vol. 19(6), pages 1-10, March.
    4. Mélissa Lemoine & Gerhard Gmel & Simon Foster & Simon Marmet & Joseph Studer, 2020. "Multiple trajectories of alcohol use and the development of alcohol use disorder: Do Swiss men mature-out of problematic alcohol use during emerging adulthood?," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    5. Benjamin Atta Owusu & Apiradee Lim & Nitinun Pongsiri & Chanthip Intawong & Sunthorn Rheanpumikankit & Saijit Suksri & Thammasin Ingviya, 2023. "Latent Trajectories of Haematological, Hepatic, and Renal Profiles after Oil Spill Exposure: A Longitudinal Analysis," IJERPH, MDPI, vol. 20(4), pages 1-14, February.
    6. Jan Vávra & Arnošt Komárek, 2023. "Classification based on multivariate mixed type longitudinal data with an application to the EU-SILC database," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 369-406, June.
    7. Jiang, Jiakun & Lin, Huazhen & Zhong, Qingzhi & Li, Yi, 2022. "Analysis of multivariate non-gaussian functional data: A semiparametric latent process approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    8. Miranda Dally & Jaime Butler-Dawson & Alex Cruz & Lyndsay Krisher & Richard J Johnson & Claudia Asensio & W Daniel Pilloni & Edwin J Asturias & Lee S Newman, 2020. "Longitudinal trends in renal function among first time sugarcane harvesters in Guatemala," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-11, March.
    9. Ye He & Ling Zhou & Yingcun Xia & Huazhen Lin, 2023. "Center‐augmented ℓ2‐type regularization for subgroup learning," Biometrics, The International Biometric Society, vol. 79(3), pages 2157-2170, September.
    10. Elena Lobo & Patricia Gracia-García & Antonio Lobo & Pedro Saz & Concepción De-la-Cámara, 2021. "Differences in Trajectories and Predictive Factors of Cognition over Time in a Sample of Cognitively Healthy Adults, in Zaragoza, Spain," IJERPH, MDPI, vol. 18(13), pages 1-13, July.
    11. Alejandra Marroig & Graciela Muniz-Terrera, 2023. "Latent Class approach to analyze children’s nutritional trajectory and school dropout. A longitudinal population-based application," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1519-1531, April.
    12. McDonough, Ian M. & Byrd, DeAnnah R. & Choi, Shinae L., 2023. "Resilience resources may buffer some middle-aged and older Black Americans from memory decline despite experiencing discrimination," Social Science & Medicine, Elsevier, vol. 316(C).
    13. Emilie Lévêque & Aude Lacourt & Viviane Philipps & Danièle Luce & Pascal Guénel & Isabelle Stücker & Cécile Proust-Lima & Karen Leffondré, 2020. "A new trajectory approach for investigating the association between an environmental or occupational exposure over lifetime and the risk of chronic disease: Application to smoking, asbestos, and lung ," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-14, August.
    14. Darío Moreno-Agostino & Alejandro de la Torre-Luque & Javier de la Fuente & Elvira Lara & Natalia Martín-María & Maria Victoria Moneta & Ivet Bayés & Beatriz Olaya & Josep Maria Haro & Marta Miret & J, 2021. "Determinants of Subjective Wellbeing Trajectories in Older Adults: A Growth Mixture Modeling Approach," Journal of Happiness Studies, Springer, vol. 22(2), pages 709-726, February.
    15. Kari R. Hart & Teng Fei & John J. Hanfelt, 2021. "Scalable and robust latent trajectory class analysis using artificial likelihood," Biometrics, The International Biometric Society, vol. 77(3), pages 1118-1128, September.

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