The World Health Organization defines cardio-vascular disease (CVD) as "a group of disorders of the heart and blood vessels," including coronary heart disease and stroke (WHO 21). CVD is affected by "intermediate risk factors" such as raised blood pressure, raised blood glucose, raised blood lipids, and obesity. These are predominantly influenced by lifestyle and behaviour, including physical inactivity, unhealthy diets, high intake of salt, and tobacco and alcohol use. However, genetics and social/environmental factors such as poverty, stress, and racism also play an important role. Researchers studying the behavioural and environmental factors associated with these "intermediate risk factors" need access to high quality and detailed information on diet and physical activity. However, missing data are a pervasive problem in clinical and public health research, affecting both randomized trials and observational studies. Reasons for missing data can vary substantially across studies because of loss to follow-up, missed study visits, refusal to answer survey questions, or an unrecorded measurement during an office visit. One method of handling missing values is to simply delete observations for which there is missingness (called Complete Case Analysis). This is rarely used as deleting the data point containing missing data (List wise deletion) results in a smaller number of samples and thus affects accuracy. Additional methods of handling missing data exists, such as summarizing the variables with its observed values (Available Case Analysis). Motivated by the pervasiveness of missing data in the NHANES dataset, we will conduct an analysis of imputation methods under different simulated patterns of missing data. We will then apply these imputation methods to create a complete dataset upon which we can use ordinary least squares to predict blood pressure from diet and physical activity.

翻译：世界卫生组织将心血管疾病定义为"心脏和血管的一组失调症状"，包括冠心病和中风（WHO 21）。心血管疾病受血压升高、血糖升高、血脂升高和肥胖等"中间风险因素"影响，这些因素主要由生活方式和行为（如缺乏运动、不健康饮食、高盐摄入、吸烟和饮酒）主导。然而，遗传及社会/环境因素（如贫困、压力、种族歧视）也发挥着重要作用。研究这些"中间风险因素"相关行为和环境因素的研究人员需要获取高质量的饮食与身体活动详细数据。然而，数据缺失是临床和公共卫生研究中普遍存在的问题，同时影响随机试验和观察性研究。不同研究中数据缺失的原因差异显著，可能源于随访中断、研究访视错过、拒绝回答调查问卷或诊室记录中未记录某项测量值。处理缺失值的一种方法是直接删除存在缺失值的观测样本（称为完整病例分析）。但这种方法很少使用，因为删除包含缺失值的数据点（列表删除）会导致样本量减少，进而影响准确性。其他处理缺失数据的方法包括使用变量的观测值进行归纳（可得病例分析）。基于NHANES数据集中数据缺失的普遍性，我们将在不同模拟缺失模式下对插补方法进行分析。随后应用这些插补方法构建完整数据集，通过普通最小二乘法从饮食和身体活动预测血压。