Abstract
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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Notes
- 1.
- 2.
The estimated g is truncated to bounds of [0.025, 0.975] as in the TMLE R-package (Gruber and van der Laan 2012a). As in the TMLE R-package, we use nonnegative least squares as the meta-learner for both Q and g.
- 3.
Blood pressure levels are defined by JNC-V (Joint National Committee 1993): optimal (systolic ≤ 120 mm Hg and diastolic ≤ 80 mm Hg), normal blood pressure (systolic 120–129 mm Hg or diastolic 80–84 mm Hg), high normal blood pressure (systolic 130–139 mm Hg or diastolic 85–89 mm Hg), hypertension stage I (systolic 140–159 mm Hg or diastolic 90–99 mm Hg), and hypertension stage II–IV (systolic ≥ 160 or diastolic ≥ 100 mm Hg). “When systolic and diastolic pressures fell into different categories, the higher category was selected for the purposes of classification.”
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Hubbard, A.E., Kennedy, C.J., van der Laan, M.J. (2018). Data-Adaptive Target Parameters. In: Targeted Learning in Data Science. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-65304-4_9
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