Data-adaptive target parameters

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Data-Adaptive Target Parameters

What factors are most important in predicting coronary heart disease? Heart disease is the leading cause of death and serious injury in the United States. To address this question we turn to the Framingham Heart Study, which was designed to investigate the health factors associated with coronary heart disease (CHD) at a time when cardiovascular disease was becoming increasingly prevalent. Starting in 1948, the prospective cohort study began monitoring a population of 5209 men and women, ages 30–62, in Framingham, Massachusetts. Those subjects received extensive medical examinations and lifestyle interviews every 2 years that provide longitudinal measurements that can be compared to outcome status. The data has been analyzed in countless observational studies and resulted in risk score equations used widely to assess risk of coronary heart disease. In our case, we conduct a comparison analysis to Wilson et al. (1998) using the data-adaptive variable importance approach described in this chapter.

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Chris Kennedy
Consulting data scientist, biostatistics PhD student