Research Article

A Bayesian Approach for Autoregressive Models in Longitudinal Data Analysis: An Application to Type 2 Diabetes Drug Comparison

The study of drug treatment remains to be an important issue to deal with high prevalence of type 2 diabetes. In this study, an auto regressive time-series framework into longitudinal data has been incorporated. The auto regressive covariance structure models have been applied on type 2 diabetes patient’s data set to study the effect of drug treatment. The simulation results suggest that the estimate of covariate based on the Markov Chain Monte Carlo (MCMC) methods are consistent compared to the estimates obtained by mixed effect models and meta analysis. The study reveals that treatment of metformin with pioglitazone for twelve months reduce the Fasting Blood Sugar (FBS) level compared to pioglitazone with gliclazide combination.

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Dilip C. Nath and Atanu Bhattacharjee, 2011. A Bayesian Approach for Autoregressive Models in Longitudinal Data Analysis: An Application to Type 2 Diabetes Drug Comparison. Asian Journal of Applied Sciences, 4: 640-648.

DOI: 10.3923/ajaps.2011.640.648

URL: https://scialert.net/abstract/?doi=ajaps.2011.640.648

Asian Journal of Applied Sciences

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Authors

Dilip C. Nath

Atanu Bhattacharjee

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Research Article

A Bayesian Approach for Autoregressive Models in Longitudinal Data Analysis: An Application to Type 2 Diabetes Drug Comparison

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