Estimating risk ratio from any standard epidemiological design by doubling the cases

Abstract

Background Despite the ease of interpretation and communication of a risk ratio (RR), and several other advantages in specific settings, the odds ratio (OR) is more commonly reported in epidemiological and clinical research. This is due to the familiarity of the logistic regression model for estimating adjusted ORs from data gathered in a cross-sectional, cohort or case-control design. The preservation of the OR (but not RR) in case-control samples has contributed to the perception that it is the only valid measure of relative risk from case-control samples. For cohort or cross-sectional data, a method known as ‘doubling-the-cases’ provides valid estimates of RR and an expression for a robust standard error has been derived, but is not available in statistical software packages. Methods In this paper, we first describe the doubling-of-cases approach in the cohort setting and then extend its application to case-control studies by incorporating sampling weights and deriving an expression for a robust standard error. The performance of the estimator is evaluated using simulated data, and its application illustrated in a study of neonatal jaundice. We provide an R package that implements the method for any standard design. Results Our work illustrates that the doubling-of-cases approach for estimating an adjusted RR from cross-sectional or cohort data can also yield valid RR estimates from case-control data. The approach is straightforward to apply, involving simple modification of the data followed by logistic regression analysis. The method performed well for case-control data from simulated cohorts with a range of prevalence rates. In the application to neonatal jaundice, the RR estimates were similar to those from relative risk regression, whereas the OR from naive logistic regression overestimated the RR despite the low prevalence of the outcome. Conclusions By providing an R package that estimates an adjusted RR from cohort, cross-sectional or case-control studies, we have enabled the method to be easily implemented with familiar software, so that investigators are not limited to reporting an OR and can examine the RR when it is of interest.

Relative Risk of Breast Cancer Associated With Prolactin-Increasing Antipsychotic Use

“…2 Following his suggestion, we have now calculated relative risk (RR) for the main results of our study, with a doubling of cases method as is recommended for the special case of nested case control studies. 3 These RRs are well in line with odds ratios (ORs) reported in our original study as can be expected when the outcome is rare, such as incident breast cancer examined in our study.…”

Identifying clinical features and blood biomarkers associated with mild cognitive impairment in Parkinson disease using machine learning

“…In the second step, the association of final variables and PD-MCI was quantified using relative risk (RR) by log-binomial regression due to the high prevalence of PD-MCI in our data, as odds ratio is likely to exaggerate the magnitude of association when the outcome is prevalent and/or when the association is strong [27].…”

“…The AIC‐based backward selection begins by including all candidate variables in the model, and improves model performance (i.e., reduces AIC) by iteratively excluding variables until no significant improvements can be made. In the second step, the association of final variables and PD‐MCI was quantified using relative risk (RR) by log‐binomial regression due to the high prevalence of PD‐MCI in our data, as odds ratio is likely to exaggerate the magnitude of association when the outcome is prevalent and/or when the association is strong [27].…”