Application of Bayesian analysis on risk factors of coronary artery disease

Ischemic heart disease (or Coronary Artery Disease) is the most common cause of death in various countries, characterized by reduced blood supply to the heart. Statistical models make an impact for evaluating the risk factors which are responsible for mortality and morbidity during IHD (Ischemic heart disease). In this work, due to count data, we propose Poisson, Negative Binomial and also utilize a flexible class of zero inflated models such as Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) models estimated by the method of MLE and are compared to assess the most appropriate model for the underlying data. The forward and backward model selection procedures are also taken to permit the most significant factors associated with heart disease. The ZIP model is identified as the most appropriate one in this work. Moreover, a Bayesian estimation is chosen to account for prior on regression coefficients in a small sample size setting. This estimation also evolves as an alternative to traditionally used MLE based methods for such data. As per our simulation studies: the proposed method has better finite sample performance than the classical method with tighter interval estimates and better coverage probabilities. The simulation is based on R-software.