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Paper   IPM / Biological Sciences / 15398
School of Biological Sciences
  Title:   A shared parameter model of longitudinal measurements and survival time with heterogeneous random-effects distribution
1.  Taban Baghfalaki
2.  Mojtaba Ganjali
3.  Geert Verbeke
  Status:   Published
  Journal: J. Appl. Statist.
  No.:  15
  Vol.:  44
  Year:  2017
  Pages:   2813-2836
  Supported by:  IPM
Typical joint modeling of longitudinal measurements and time to event data assumes that two models share a common set of random effects with a normal distribution assumption. But, sometimes the underlying population that the sample is extracted from is a heterogeneous population and detecting homogeneous subsamples of it is an important scientific question. In this paper, a finite mixture of normal distributions for the shared random effects is proposed for considering the heterogeneity in the population. For detecting whether the unobserved heterogeneity exits or not, we use a simple graphical exploratory diagnostic tool proposed by Verbeke and Molenberghs [34] to assess whether the traditional normality assumption for the random effects in the mixed model is adequate. In the joint modeling setting, in the case of evidence against normality (homogeneity), a finite mixture of normals is used for the shared random-effects distribution. A Bayesian MCMC procedure is developed for parameter estimation and inference. The methodology is illustrated using some simulation studies. Also, the proposed approach is used for analyzing a real HIV data set, using the heterogeneous joint model for this data set, the individuals are classified into two groups: a group with high risk and a group with moderate risk.

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