American Journal of Epidemiology Vol. 148, No. 4: 390-401
Copyright © 1998 by The Johns Hopkins University School of Hygiene and Public Health
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G-estimation of Causal Effects: Isolated Systolic Hypertension and Cardiovascular Death in the Framingham Heart Study
1Department of Epidemiology and Biostatistics, Erasmus University Medical School Rotterdam, Netherlands
2Department of Mathematics, Boston University College of Liberal Arts Boston, MA
3Section of Preventive Medicine and Epidemiology, Boston University School of Medicine Boston, MA
4Departments of Epidemiology and Biostatistics, Harvard School of Public Health Boston, MA
Reprint requests to Dr. J. C. M. Witteman, Department of Epidemiology and Biostatistics, Erasmus University Medical School, P.O.Box 1738, 3000 DR Rotterdam, Netherlands.
Time-dependent covariates are often both confounders and intermediate variables. In the presence of such covariates, standard approaches for adjustment for confounding are biased. The method of G-estimation allows for appropriate adjustment. Previous studies applying the G-estimation method have addressed effects on all-cause mortality rather than on specific causes of death. In the present study, a method to adjust for censoring by competing risks is presented. The authors used the approach to estimate the causal effect of isolated systolic hypertension on cardiovascular mortality in the Framingham Heart Study, with a 10-year follow-up using data from 1956 to 1970. Arterial rigidity is a major determinant of isolated systolic hypertension and may be a confounder of the relation between isolated systolic hypertension and cardiovascular death. Conversely, isolated systolic hypertension may by itself contribute to stiffening of the vessel wall, and arterial rigidity may therefore also be an intermediate variable in the causal pathway from isolated systolic hypertension to cardiovascular death. While controlling for arterial rigidity and other baseline and time-dependent covariates, isolated systolic hypertension decreased the time to cardiovascular death by 45% (95% confidence interval 3–69). Am J Epidemiol 1998; 148: 390–401.
bias (epidemiology); blood pressure; cardiovascular diseases; epidemiologic methods; follow-up studies; models; statistical; statistics
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