Abstract
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 in evaluating the risk factors that are responsible for mortality and morbidity during IHD (Ischemic heart disease). In general, geometric or Poisson distributions can underestimate the zero-count probability and hence make it difficult to identify significant effects of covariates for improving conditions of heart disease due to regional wall motion abnormalities. In this work, a flexible class of zero inflated models is introduced. A Bayesian estimation method is developed as an alternative to traditionally used maximum likelihood-based methods to analyze such data. Simulation studies show that the proposed method has a better small sample performance than the classical method, with tighter interval estimates and better coverage probabilities. Although the prevention of CAD has long been a focus of public health policy, clinical medicine, and biomedical scientific investigation, the prevalence of CAD remains high despite current strategies for prevention and treatment. Various comprehensive searches have been performed in the MEDLINE, HealthSTAR, and Global Health databases for providing insights into the effects of traditional and emerging risk factors of CAD. A real-life data set is illustrated for the proposed method using WinBUGS.