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A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data

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  • Cécile Proust
  • Hélène Jacqmin-Gadda
  • Jeremy M. G. Taylor
  • Julien Ganiayre
  • Daniel Commenges

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  • 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.
    • Handle: RePEc:bla:biomet:v:62:y:2006:i:4:p:1014-1024
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2006.00573.x
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    References listed on IDEAS

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    1. Dunson, David B., 2003. "Dynamic Latent Trait Models for Multidimensional Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 555-563, January.
    2. N. Longford & B. Muthén, 1992. "Factor analysis for clustered observations," Psychometrika, Springer;The Psychometric Society, vol. 57(4), pages 581-597, December.
    3. Gerhard Arminger & Bengt Muthén, 1998. "A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolis-hastings algorithm," Psychometrika, Springer;The Psychometric Society, vol. 63(3), pages 271-300, September.
    4. D. B. Dunson, 2000. "Bayesian latent variable models for clustered mixed outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 355-366.
    5. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "Generalized multilevel structural equation modeling," Psychometrika, Springer;The Psychometric Society, vol. 69(2), pages 167-190, June.
    6. Jason Roy & Xihong Lin, 2000. "Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes," Biometrics, The International Biometric Society, vol. 56(4), pages 1047-1054, December.
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    Citations

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    Cited by:

    1. 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).
    2. Dantan Etienne & Proust-Lima Cécile & Letenneur Luc & Jacqmin-Gadda Helene, 2008. "Pattern Mixture Models and Latent Class Models for the Analysis of Multivariate Longitudinal Data with Informative Dropouts," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-28, July.
    3. Daniel Commenges, 2019. "Dealing with death when studying disease or physiological marker: the stochastic system approach to causality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 381-405, July.
    4. Peter Hall & Hans‐Georg Müller & Fang Yao, 2008. "Modelling sparse generalized longitudinal observations with latent Gaussian processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 703-723, September.
    5. 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.
    6. 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.
    7. Dongbing Lai & Huiping Xu & Daniel Koller & Tatiana Foroud & Sujuan Gao, 2016. "A multivariate finite mixture latent trajectory model with application to dementia studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2503-2523, October.
    8. Commenges Daniel & Proust-Lima Cécile & Samieri Cécilia & Liquet Benoit, 2015. "A Universal Approximate Cross-Validation Criterion for Regular Risk Functions," The International Journal of Biostatistics, De Gruyter, vol. 11(1), pages 51-67, May.
    9. 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).
    10. B. N. Sánchez & E. A. Houseman & L. M. Ryan, 2009. "Residual-Based Diagnostics for Structural Equation Models," Biometrics, The International Biometric Society, vol. 65(1), pages 104-115, March.
    11. 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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