The Upper Limits of Risk Ratios and Recommendations for Reporting Risk Ratios, Odds Ratios, and Rate Ratios.

Autor:	Chao YS; Epidemiology and Public Health, Independent Researcher, Montreal, CAN., Wu CJ; Computer Science, Université du Québec à Montréal, Montreal, CAN., Po JY; Natural Resources Institute, University of Greenwich, London, GBR., Huang SY; Anesthesiology, Shuang Ho Hospital, New Taipei, TWN., Wu HC; Internal Medicine, National Taiwan University Hospital Jinshan Branch, New Taipei, TWN., Hsu HT; Pathology, Changhua Christian Hospital, Changhua, TWN., Cheng YP; Neurological Surgery, Changhua Christian Hospital, Changhua, TWN., Lai YC; Chest Medicine, National Yang Ming Chiao Tung University Hospital, Yilan, TWN., Chen WC; Chest Medicine, Taipei Veterans General Hospital, Taipei, TWN.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2023 Apr 18; Vol. 15 (4), pp. e37799. Date of Electronic Publication: 2023 Apr 18 (Print Publication: 2023).
DOI:	10.7759/cureus.37799
Abstrakt:	Background Relative measures, including risk ratios (RRs) and odds ratios (ORs), are reported in many epidemiological studies. RRs represent how many times a condition is likely to develop when exposed to a risk factor. The upper limit of RRs is the multiplicative inverse of the baseline incidence. Ignoring the upper limits of RRs can lead to reporting exaggerated relative effect sizes. Objectives This study aims to demonstrate the importance of such upper limits for effect size reporting via equations, examples, and simulations and provide recommendations for the reporting of relative measures. Methods Equations to calculate RRs and their 95% confidence intervals (CIs) were listed. We performed simulations with 10,000 simulated subjects and three population variables: proportions at risk (0.05, 0.1, 0.3, 0.5, and 0.8), baseline incidence (0.05, 0.1, 0.3, 0.5, and 0.8), and RRs (0.5, 1.0, 5.0, 10.0, and 25.0). Subjects were randomly assigned with a risk based on the set of proportions-at-risk values. A disease occurred based on the baseline incidence among those not at risk. The incidence of those at risk was the product of the baseline incidence and the RRs. The 95% CIs of RRs were calculated according to Altman. Results The calculation of RR 95% CIs is not connected to the RR upper limits in equations. The RRs in the simulated populations at risk could reach the upper limits of RRs: multiplicative inverse of the baseline incidence. The upper limits to the derived RRs were around 1.25, 2, 3.3, 10, and 20, when the assumed baseline incidence rates were 0.8, 0.5, 0.3, 0.2, and 0.05, respectively. We demonstrated five scenarios in which the RR 95% CIs might exceed the upper limits. Conclusions Statistical significance does not imply the RR 95% CIs not exceeding the upper limits of RRs. When reporting RRs or ORs, the RR upper limits should be assessed. The rate ratio is also subject to a similar upper limit. In the literature, ORs tend to overestimate effect sizes. It is recommended to correct ORs that aim to approximate RRs assuming outcomes are rare. A reporting guide for relative measures, RRs, ORs, and rate ratios, is provided. Researchers are recommended to report whether the 95% CIs of relative measures, RRs, ORs, and rate ratios, overlap with the range of upper limits and discuss whether the relative measure estimates may exceed the upper limits. Competing Interests: YSC is currently employed by the Canadian Agency for Drugs and Technologies in Health. CJW, HCW, HTH, YPC, YCL, JYTP, SYH, and WCC declare that they have no competing interests. YSC conducted this study as an independent researcher out of academic curiosity without any material support. No external funding was received for this study. This study is not associated with any patents, products in development, or marketed products. (Copyright © 2023, Chao et al.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu