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.

中文翻译：

---

缺血性心脏病心血管危险因素的贝叶斯分析

缺血性心脏病（或冠状动脉疾病）是各国最常见的死亡原因，其特征是心脏供血减少。统计模型对评估导致 IHD（缺血性心脏病）死亡率和发病率的风险因素产生影响。一般来说，几何或泊松分布会低估零计数概率，因此难以确定协变量对改善因局部室壁运动异常引起的心脏病状况的显着影响。在这项工作中，引入了一类灵活的零膨胀模型。贝叶斯估计方法被开发作为传统使用的基于最大似然的方法的替代来分析此类数据。仿真研究表明，所提出的方法比经典方法具有更好的小样本性能，具有更紧密的区间估计和更好的覆盖概率。尽管 CAD 的预防长期以来一直是公共卫生政策、临床医学和生物医学科学研究的重点，但尽管采用了当前的预防和治疗策略，但 CAD 的患病率仍然很高。在 MEDLINE、HealthSTAR 和 Global Health 数据库中进行了各种综合搜索，以深入了解传统和新兴风险因素对 CAD 的影响。为使用 WinBUGS 提出的方法说明了真实数据集。临床医学和生物医学科学研究，尽管目前采取了预防和治疗策略，但 CAD 的患病率仍然很高。在 MEDLINE、HealthSTAR 和 Global Health 数据库中进行了各种综合搜索，以深入了解传统和新兴风险因素对 CAD 的影响。为使用 WinBUGS 提出的方法说明了真实数据集。临床医学和生物医学科学研究，尽管目前采取了预防和治疗策略，但 CAD 的患病率仍然很高。在 MEDLINE、HealthSTAR 和 Global Health 数据库中进行了各种综合搜索，以深入了解传统和新兴风险因素对 CAD 的影响。为使用 WinBUGS 提出的方法说明了真实数据集。