Suhail A Doi1, Luis Furuya-Kanamori2, Chang Xu3, Lifeng Lin4, Tawanda Chivese5, Lukman Thalib6. 1. Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar. Electronic address: sardoi@gmx.net. 2. Research School of Population Health, College of Health and Medicine, Australian National University, Canberra, Australia. 3. Chinese Evidence-Based Medicine Center, Sichuan University, Chengdu, China. 4. Department of Statistics, Florida State University, Tallahassee, FL, USA. 5. Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar. 6. Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.
Abstract
BACKGROUND AND OBJECTIVES: In clinical trials, the relative risk or risk ratio (RR) is a mainstay of reporting of the effect magnitude for an intervention. The RR is the ratio of the probability of an outcome in an intervention group to its probability in a control group. Thus, the RR provides a measure of change in the likelihood of an event linked to a given intervention. This measure has been widely used because it is today considered a measure with "portability" across varying outcome prevalence, especially when the outcome is rare. It turns out, however, that there is a much more important problem with this ratio, and this paper aims to demonstrate this problem. METHODS: We used mathematical derivation to determine if the RR is a measure of effect magnitude alone (i.e., a larger absolute value always indicating a stronger effect) or not. We also used the same derivation to determine its relationship to the prevalence of an outcome. We confirm the derivation results with a follow-up analysis of 140,620 trials scraped from the Cochrane. RESULTS: We demonstrate that the RR varies for reasons other than the magnitude of the effect because it is a ratio of two posterior probabilities, both of which are dependent on baseline prevalence of an outcome. In addition, we demonstrate that the RR shifts toward its null value with increasing outcome prevalence. The shift toward the null happens regardless of the strength of the association between intervention and outcome. The odds ratio (OR), the other commonly used ratio, measures solely the effect magnitude and has no relationship to the prevalence of an outcome in a study nor does it overestimate the RR as is commonly thought. CONCLUSIONS: The results demonstrate the need to (1) end the primary use of the RR in clinical trials and meta-analyses as its direct interpretation is not meaningful, (2) replace the RR by the OR, and (3) only use the postintervention risk recalculated from the OR for any expected level of baseline risk in absolute terms for purposes of interpretation such as the number needed to treat. These results will have far-reaching implications such as reducing misleading results from clinical trials and meta-analyses and ushering in a new era in the reporting of such trials or meta-analyses in practice.
BACKGROUND AND OBJECTIVES: In clinical trials, the relative risk or risk ratio (RR) is a mainstay of reporting of the effect magnitude for an intervention. The RR is the ratio of the probability of an outcome in an intervention group to its probability in a control group. Thus, the RR provides a measure of change in the likelihood of an event linked to a given intervention. This measure has been widely used because it is today considered a measure with "portability" across varying outcome prevalence, especially when the outcome is rare. It turns out, however, that there is a much more important problem with this ratio, and this paper aims to demonstrate this problem. METHODS: We used mathematical derivation to determine if the RR is a measure of effect magnitude alone (i.e., a larger absolute value always indicating a stronger effect) or not. We also used the same derivation to determine its relationship to the prevalence of an outcome. We confirm the derivation results with a follow-up analysis of 140,620 trials scraped from the Cochrane. RESULTS: We demonstrate that the RR varies for reasons other than the magnitude of the effect because it is a ratio of two posterior probabilities, both of which are dependent on baseline prevalence of an outcome. In addition, we demonstrate that the RR shifts toward its null value with increasing outcome prevalence. The shift toward the null happens regardless of the strength of the association between intervention and outcome. The odds ratio (OR), the other commonly used ratio, measures solely the effect magnitude and has no relationship to the prevalence of an outcome in a study nor does it overestimate the RR as is commonly thought. CONCLUSIONS: The results demonstrate the need to (1) end the primary use of the RR in clinical trials and meta-analyses as its direct interpretation is not meaningful, (2) replace the RR by the OR, and (3) only use the postintervention risk recalculated from the OR for any expected level of baseline risk in absolute terms for purposes of interpretation such as the number needed to treat. These results will have far-reaching implications such as reducing misleading results from clinical trials and meta-analyses and ushering in a new era in the reporting of such trials or meta-analyses in practice.
Authors: Mengli Xiao; Haitao Chu; Stephen R Cole; Yong Chen; Richard F MacLehose; David B Richardson; Sander Greenland Journal: J Clin Epidemiol Date: 2021-08-11 Impact factor: 6.437
Authors: Daniel L Belavy; Scott D Tagliaferri; Paul Buntine; Tobias Saueressig; Katja Ehrenbrusthoff; Xiaolong Chen; Ashish Diwan; Clint T Miller; Patrick J Owen Journal: Eur Spine J Date: 2022-09-17 Impact factor: 2.721
Authors: Tawanda Chivese; Joshua T Matizanadzo; Omran A H Musa; George Hindy; Luis Furuya-Kanamori; Nazmul Islam; Rafal Al-Shebly; Rana Shalaby; Mohammad Habibullah; Talal A Al-Marwani; Rizeq F Hourani; Ahmed D Nawaz; Mohammad Z Haider; Mohamed M Emara; Farhan Cyprian; Suhail A R Doi Journal: Pathog Glob Health Date: 2022-01-31 Impact factor: 3.735
Authors: Mengli Xiao; Yong Chen; Stephen R Cole; Richard F MacLehose; David B Richardson; Haitao Chu Journal: J Clin Epidemiol Date: 2021-08-09 Impact factor: 6.437