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

Dilip C. Nath and Atanu Bhattacharjee

Statistics in Transition new series, 2014, vol. 15, issue 3, 437-452

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.

Keywords: random effects; semi-parametric survival model; Weibull distribution; linked sub-models (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:csb:stintr:v:15:y:2014:i:3:p:437-452

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