Bayesian Inference in a Joint Model for Longitudinal and Time to Event Data with Gompertz Baseline Hazards

Longitudinal and time to event data are frequently encountered in many medical studies. Clinicians are more interested in how longitudinal outcomes influences the time to an event of i nterest. To study the association between longitudinal and time to event data, joint modeling approaches were found to be the most appropriate techniques for such data. The approaches involves the choice of the distribution of the survival times which in most cases authors prefer either exponential or Weibull distribution. However, these distributions have some shortcomings. In this paper, we propose an alternative joint model approach under Bayesian prospective. We assumed that survival times follow a Gompertz distribution. One of the advantages of Gompertz distribution is that its cumulative distribution function has a closed form solution and it accommodates time varying covariates. A Bayesian approach through Gibbs sampling procedure was developed for parameter estimation and inferences. We evaluate the finite samples performance of the joint model through an extensive simulation study and apply the model to a real dataset to determine the association between markers(tumor sizes) and time to death among cancer patients without recurrence. Our analysis suggested that the proposed joint modeling approach perform well in terms of parameter estimations when correlation between random intercepts and slopes is considered.

• Publication year

  2018

• Venue

  Modern Applied Science

• Publication date

  2018-08-24

• Fields of study

  Medicine, Mathematics, Computer Science

• Identifiers
  DOI 10.5539/MAS.V12N9P159
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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