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IJE Advance Access published online on April 23, 2009
International Journal of Epidemiology, doi:10.1093/ije/dyp192
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2009; all rights reserved.

Intervening on risk factors for coronary heart disease: an application of the parametric g-formula

Sarah L Taubman1,2,*, James M Robins2,3, Murray A Mittleman2,4 and Miguel A Hernán2,5

1National Bureau of Economic Research, Cambridge, MA, USA.
2Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
3Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
4Cardiovascular Epidemiology Research Unit, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
5Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.

* Corresponding author. National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA 02138, USA. E-mail: staub{at}nber.org


  Abstract

Estimating the population risk of disease under hypothetical interventions—such as the population risk of coronary heart disease (CHD) were everyone to quit smoking and start exercising or to start exercising if diagnosed with diabetes—may not be possible using standard analytic techniques. The parametric g-formula, which appropriately adjusts for time-varying confounders affected by prior exposures, is especially well suited to estimating effects when the intervention involves multiple factors (joint interventions) or when the intervention involves decisions that depend on the value of evolving time-dependent factors (dynamic interventions). We describe the parametric g-formula, and use it to estimate the effect of various hypothetical lifestyle interventions on the risk of CHD using data from the Nurses’ Health Study. Over the period 1982–2002, the 20-year risk of CHD in this cohort was 3.50%. Under a joint intervention of no smoking, increased exercise, improved diet, moderate alcohol consumption and reduced body mass index, the estimated risk was 1.89% (95% confidence interval: 1.46–2.41). We discuss whether the assumptions required for the validity of the parametric g-formula hold in the Nurses’ Health Study data. This work represents the first large-scale application of the parametric g-formula in an epidemiologic cohort study.

Keywords g-formula, coronary heart disease, hypothetical interventions

Accepted 17 March 2009


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