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Adjustment for multiple cardiovascular risk factors using a summary risk score
Patrick G Arbogast1, Lisa Kaltenbach, Hua Ding
1Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37232-2158, USA. patrick.arbogast@vanderbilt.edu
Insights
Using a cardiovascular risk score effectively adjusts for multiple risk factors in large cohort studies. This method provides reliable standard errors, ensuring accurate statistical significance when assessing exposures.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Pharmacoepidemiologic studies often use cardiovascular risk scores to adjust for multiple confounding factors.
- These scores are multivariate confounder summaries derived from regression models.
- Concerns exist regarding potential underestimation of standard errors and inflated statistical significance when using risk scores.
Purpose of the Study:
- To compare the performance of regression models using a summary cardiovascular risk score versus direct adjustment for individual risk factors.
- To evaluate the accuracy of standard error estimates in models employing a summary risk score.
Main Methods:
- Conducted simulation studies for large cohort data.
- Compared regression models that directly adjust for all risk factors against those using a summary cardiovascular risk score.
Main Results:
- Estimated standard errors from models using the summary risk score closely approximated empirical standard errors.
- These estimates were similar to standard errors obtained from models directly adjusting for all risk factors.
- This holds true provided there is no high intercorrelation between confounders and the exposure.
Conclusions:
- The summary cardiovascular risk score is a reasonable approach for managing multiple risk factors in large cohort studies.
- It offers a practical method for adjusting for confounding without compromising the reliability of standard error estimates.
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.
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