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Initial classification of joint data in EM estimation of latent class joint model

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  • Han, Jun

Abstract

The latent class mixture-of-experts joint model is one of the important methods for jointly modelling longitudinal and recurrent events data when the underlying population is heterogeneous and there are nonnormally distributed outcomes. The maximum likelihood estimates of parameters in latent class joint model are generally obtained by the EM algorithm. The joint distances between subjects and initial classification of subjects under study are essential to finding good starting values of the EM algorithm through formulas. In this article, separate distances and joint distances of longitudinal markers and recurrent events are proposed for classification purposes, and performance of the initial classifications based on the proposed distances and random classification are compared in a simulation study and demonstrated in an example.

Suggested Citation

  • Han, Jun, 2009. "Initial classification of joint data in EM estimation of latent class joint model," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2313-2323, November.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:10:p:2313-2323
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    References listed on IDEAS

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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. 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.
    3. Haiqun Lin & Charles E. McCulloch & Robert A. Rosenheck, 2004. "Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies," Biometrics, The International Biometric Society, vol. 60(2), pages 295-305, June.
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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.

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