Estimating the effect of cardiovascular risk factors on all-cause mortality and incidence of coronary heart disease using G-estimation: The Atherosclerosis Risk in Communities study

Standard methods for analysis of cohort studies may give biased estimates of exposure effects in the presence of time-varying confounding. Such effects may instead be estimated by using G-estimation. This study aimed to examine the relations between important cardiovascular risk factors and all-cause mortality and risk of coronary heart disease (CHD), accounting for confounding between exposures over time using G-estimation. Results were compared with those from standard survival analyses (e.g., Weibull regression) with time-updated covariates. The dataset consisted of all participants in the Atherosclerosis Risk in Communities cohort study who had complete data on the first two of four visits, giving a sample of 13,898 people at baseline. Death and occurrence of CHD or stroke were recorded. G-estimated associations between several risk factors and mortality/CHD incidence differed from those estimated using standard survival analysis. The associations between mortality/CHD incidence and smoking, presence of diabetes, and use of antihypertensives were stronger than the standard survival estimates, while the G-estimated effect of low density lipoprotein and high density lipoprotein cholesterol on CHD incidence were more linear than the standard estimate. Complex relations between exposures over time may lead to biased exposure effect estimates in standard survival analyses. G-Estimation can be used to overcome such biases, and thus may have important implications for the analysis of observational studies.