  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.
