A Bayesian Joint Analysis and Imputation Model for Longitudinal Data: An Application in Type 2 Diabetes Drug Effect Comparison

Background: The level of serum creatinine is important affected parameter in presence of type 2 diabetes. The choice of type 2 diabetes drug therapy is crucial to control the serum creatinine level. The drug treatment effect can only be captured through repeated observations in the patients. Objective: The aim of this work is to compare the drug treatment effect (i.e. “Metformin plus Pioglitazone” and “Gliclazide plus Pioglitazone”) in presences of repeatedly measured missing observations to control serum cretinine levels in type 2 diabetes patients. Method: The joint longitudinal modeling approach is applied to deal with missing observations. The presences of missing observations are assigned with missing at random and not random. The Markov chain Monte Carlo (MCMC) is used to carry out the iteration procedure. Results: The “Metformin plus Pioglitazone” is found more effective to control serum creatinine in comparison to “Gliclazide with Pioglitazone”. The joint longitudinal model with consideration of missing assumption proffers enhanced tool for inference on clinical trial data analysis. Conclusion: The presence of missing observation is natural in repeated measurement. The tendency is to overlook the trial having observation and conclusion with missing observation. The elaborated method can be applied in other clinical trial problem to reduce the inconsistency due presence of missing observations.