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A Latent Process Model for Joint Modeling of Events and Marker

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Abstract

The paper formulates joint modeling of a counting process and a sequence of longitudinal measurements, governed by a common latent stochastic process. The latent process is modeled as a function of explanatory variables and a Brownian motion process. The conditional likelihood given values of the latent process at the measurement times, has been drawn using Brownian bridge properties; then integrating over all possible values of the latent process at the measurement times leads to the desired joint likelihood. An estimation procedure using joint likelihood and a numerical optimization is described. The method is applied to the study of cognitive decline and Alzheimer's disease.

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Authors and Affiliations

  1. Faculty of Science, Department of Statistics, Razi University, Bagh-e-Abrisham, Kermanshah, Iran

    R. Hashemi

  2. INSERM E03 38, 146 rue Léo Saignat, 33076, Bordeaux, France

    H. Jacqmin-Gadda & D. Commenges

Authors
  1. R. Hashemi
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  2. H. Jacqmin-Gadda
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  3. D. Commenges
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Hashemi, R., Jacqmin-Gadda, H. & Commenges, D. A Latent Process Model for Joint Modeling of Events and Marker. Lifetime Data Anal 9, 331–343 (2003). https://doi.org/10.1023/B:LIDA.0000012420.36627.a6

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  • DOI: https://doi.org/10.1023/B:LIDA.0000012420.36627.a6

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