Background: To simultaneously adjust for confounding by multiple cardiovascular risk factors, recently published pharmacoepidemiologic studies have used an index of risk of cardiovascular disease (a cardiovascular risk score). This summary measure is a multivariate confounder score created from regression models relating these risk factors to the outcome. The score is then used in regression models to adjust for potential confounding of the exposure of interest. Although this summary score has a number of advantages, there is concern that it may result in underestimation of the standard error of the exposure estimate and thus inflate the number of statistically significant results.
Methods: We conducted simulation studies comparing regression models adjusting for all risk factors directly to models using this summary risk score for large cohort studies.
Results: Results indicated that, as long as there was not a high degree of intercorrelation between the potential confounders and the exposure, estimated standard errors from the regression models using this summary risk score approximate their empirical standard errors well and are similar to the standard errors from the regression models directly adjusting for all risk factors.
Conclusions: Based on these simulation results, using this summary risk score can be a reasonable approach for summarizing many risk factors in large cohort studies.