Re‐examination of statistical relationships between dietary fats and other risk factors, and cardiovascular disease, based on two crucial datasets

Cardiovascular disease (CVD) is the major cause of death in many regions around the world, and several of its risk factors might be linked to diets. To improve public health and the understanding of this topic, we look at the recent Minnesota Coronary Experiment (MCE) analysis that used t‐test and Cox model to evaluate CVD risks. However, these parametric methods might suffer from three problems: small sample size, right‐censored bias, and lack of long‐term evidence. To overcome the first of these challenges, we utilize a nonparametric permutation test to examine the relationship between dietary fats and serum total cholesterol. To address the second problem, we use a resampling‐based rank test to examine whether the serum total cholesterol level affects CVD deaths. For the third issue, we use some extra‐Framingham Heart Study (FHS) data with an A/B test to look for meta‐relationship between diets, risk factors, and CVD risks. We show that, firstly, the link between low saturated fat diets and reduction in serum total cholesterol is strong. Secondly, reducing serum total cholesterol does not robustly have an impact on CVD hazards in the diet group. Lastly, the A/B test result suggests a more complicated relationship regarding abnormal diastolic blood pressure ranges caused by diets and how these might affect the associative link between the cholesterol level and heart disease risks. This study not only helps us to deeply analyze the MCE data but also, in combination with the long‐term FHS data, reveals possible complex relationships behind diets, risk factors, and heart disease.