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A multivariate finite mixture latent trajectory model with application to dementia studies

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Listed:
  • Dongbing Lai
  • Huiping Xu
  • Daniel Koller
  • Tatiana Foroud
  • Sujuan Gao

Abstract

Dementia patients exhibit considerable heterogeneity in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remaining stable for several years. Dementia studies often collect longitudinal measures of multiple neuropsychological tests aimed to measure patients’ decline across a number of cognitive domains. We propose a multivariate finite mixture latent trajectory model to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests. EM algorithm is used for parameter estimation and posterior probabilities are used to predict latent class membership. We present results of a simulation study demonstrating adequate performance of our proposed approach and apply our model to the Uniform Data Set from the National Alzheimer's Coordinating Center to identify cognitive decline patterns among dementia patients.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2503-2523
    DOI: 10.1080/02664763.2016.1141181
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    References listed on IDEAS

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    1. 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.
    2. Yang, Chih-Chien, 2006. "Evaluating latent class analysis models in qualitative phenotype identification," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1090-1104, February.
    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.
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    Cited by:

    1. Wei Zhao & Limin Peng & John Hanfelt, 2022. "Semiparametric latent class analysis of recurrent event data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1175-1197, September.
    2. Alberto Bucci & Lorenzo Carbonari & Monia Ranalli & Giovanni Trovato, 2019. "Health and Development," CEIS Research Paper 470, Tor Vergata University, CEIS, revised 24 Mar 2021.

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