A simulation study of regression approaches for estimating risk ratios in the presence of multiple confounders.

ABSTRACT: Risk ratio is a popular effect measure in epidemiological research. Although previous research has suggested that logistic regression may provide biased odds ratio estimates when the number of events is small and there are multiple confounders, the performance of risk ratio estimation has yet to be examined in the presence of multiple confounders. We conducted a simulation study to evaluate the statistical performance of three regression approaches for estimating risk ratios: (1) risk ratio interpretation of logistic regression coefficients, (2) modified Poisson regression, and (3) regression standardization using logistic regression. We simulated 270 scenarios with systematically varied sample size, the number of binary confounders, exposure proportion, risk ratio, and outcome proportion.

SUBMITTER: Fuyama K 

PROVIDER: S-EPMC8665581 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature