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Joint longitudinal and survival data modelling: an application in anti-diabetes drug therapeutic effect

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  • Dilip C. Nath
  • Atanu Bhattacharjee

Abstract

The longitudinal and survival analyses are useful tools in the exploration of drug trial data. In both cases the challenge is to deal with correlated repeated observations. Here, the joint modelling for longitudinal and survival data has been carried out via Markov chain Monte Carlo (MCMC) method in type 2 diabetes clinical trials to compare different combinations of drugs, viz. Metformin plus Pioglitazone and Gliclazide plus Pioglitazone. Despite the complexity of the model it has been found relatively easier to implement with WinBugs software. The results have been computed and compared with software R. In both types of the analyses it has been found that no estimates of treatment appear to have significant effect on the evolution of the matter of HBA1c, neither on the longitudinal part nor on the survival one. The Bayesian approach has been considered as an extended tool with classical approach for estimation of clinical trial data analysis.

Suggested Citation

  • Dilip C. Nath & Atanu Bhattacharjee, 2014. "Joint longitudinal and survival data modelling: an application in anti-diabetes drug therapeutic effect," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 437-452, June.
    • Handle: RePEc:csb:stintr:v:15:y:2014:i:3:p:437-452
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    References listed on IDEAS

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    1. Robert M. Elashoff & Gang Li & Ning Li, 2008. "A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types," Biometrics, The International Biometric Society, vol. 64(3), pages 762-771, September.
    2. Yueh-Yun Chi & Joseph G. Ibrahim, 2006. "Joint Models for Multivariate Longitudinal and Multivariate Survival Data," Biometrics, The International Biometric Society, vol. 62(2), pages 432-445, June.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Guo X. & Carlin B.P., 2004. "Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages," The American Statistician, American Statistical Association, vol. 58, pages 16-24, February.
    5. Liang Li & Bo Hu & Tom Greene, 2009. "A Semiparametric Joint Model for Longitudinal and Survival Data with Application to Hemodialysis Study," Biometrics, The International Biometric Society, vol. 65(3), pages 737-745, September.
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