Discrimination and conversion between hazard ratio and risk ratio as effect measures in prospective studies_Chinese Journal of Evidence-Based Medicine

Authors：

HUANG Qiao ^1,2,3 , ZHAO Mingjuan ^1,2,3 , LUO Lisha ^1,2,3 , DENG Tong ^1,2,3 ,  ZENG Xiantao ^1,2,3,4 ,  WANG Xinghuan ^1,2,4

1. Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, P.R.China;
2. Center for Evidence-Based and Translational Medicine, Wuhan University, Wuhan 430071, P.R.China;
3. Department of Evidence-Based Medicine and Clinical Epidemiology, The Second Clinical College, Wuhan University, Wuhan 430071, P.R.China;
4. Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan 430071, P.R.China;

Corresponding author：

ZENG Xiantao, Email: zengxiantao1128@163.com; WANG Xinghuan, Email: wangxinghuan1965@163.com

Keywords：

Prospective study; Risk ratio; Hazard ratio; Conversion

DOI：

10.7507/1672-2531.202005092

Video：

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Abstract Full text Figures/Tables Video References Cited by

Risk ratio (RR) and hazard ratio (HR) are two common effect measures in prospective studies. When describing the magnitude of clinical effects in the original study and meta-analysis, they cannot be used interchangeably. Selecting an appropriate measures and interpreting them correctly is critical in clinical research. In this study, we summarized similarities and differences between risk and hazard, compared differences between RR and HR in estimation methods and clinical interpretation. The magnitude of RR and HR estimated from the same studies were compared, and two feasible formulas converting between RR and HR were presented for meta-analysis.

Citation： HUANG Qiao, ZHAO Mingjuan, LUO Lisha, DENG Tong, ZENG Xiantao, WANG Xinghuan. Discrimination and conversion between hazard ratio and risk ratio as effect measures in prospective studies. Chinese Journal of Evidence-Based Medicine, 2020, 20(10): 1221-1225. doi: 10.7507/1672-2531.202005092 Copy

1.	Austin PC. Absolute risk reductions, relative risks, relative risk reductions, and numbers needed to treat can be obtained from a logistic regression model. J Clin Epidemiol, 2010, 63(1): 2-6.
2.	Zhang J, Yu KF. What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA, 1998, 280(19): 1690-1691.
3.	贺腾, 刘黎明, 曹汝, 等. 多靶点酪氨酸激酶抑制剂联合化疗与单纯化疗比较治疗晚期非小细胞肺癌疗效和安全性的 Meta 分析. 中国循证医学杂志, 2017, 17(10): 1198-1204.
4.	黄桥, 邝心颖, 童铁军, 等. 基于中位数为效应量的 meta 分析及 R 实现. 医学新知, 2020, 30(4): 265-271.
5.	Saczynski JS, Spencer FA, Gore JM, et al. Twenty-year trends in the incidence of stroke complicating acute myocardial infarction: Worcester Heart Attack Study. Arch Intern Med, 2008, 168(19): 2104-2110.
6.	Austin PC. The performance of different propensity-score methods for estimating relative risks. J Clin Epidemiol, 2008, 61(6): 537-545.
7.	付志智, 邓革红, 蔡富文, 等. 应用 log-binomial 回归和 logistic 回归分析法定传染病报告质量影响因素. 疾病监测, 2017, 32(9): 768-773.
8.	高文龙, 刘小宁, 颜虹. Log-binomial 回归在社区干预效果评价中的应用. 中国卫生统计, 2014, 31(3): 486-487, 491.
9.	Deddens JA, Petersen MR, Lei X. Estimation of prevalence ratios when PROC GENMOD does not converge. SAS User Group International Proceedings Paper, 2003: 270-280.
10.	Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol, 2004, 159(7): 702-706.
11.	Austin PC. Absolute risk reductions and numbers needed to treat can be obtained from adjusted survival models for time-to-event outcomes. J Clin Epidemiol, 2010, 63(1): 46-55.
12.	Breslow N. Covariance analysis of censored survival data. Biometrics, 1974, 30(1): 89-99.
13.	郜艳晖, 周舒冬, 李丽霞, 等. 基于相对危险度/患病率比的模型及参数估计方法研究进展. 中华流行病学杂志, 2013, 34(9): 935-939.
14.	Fang J. Using SAS® procedures FREQ, GENMOD, LOGISTIC and PHREG to estimate adjusted relative risks-a case study. SAS Global Forum, 2011: 345.
15.	Shor E, Roelfs D, Vang ZM. The "Hispanic mortality paradox" revisited: Meta-analysis and meta-regression of life-course differentials in Latin American and Caribbean immigrants' mortality. Soc Sci Med, 2017, 186: 20-33.
16.	VanderWeele TJ. On a Square-Root transformation of the odds ratio for a common outcome. Epidemiology, 2017, 28(6): e58-e60.
17.	VanderWeele TJ. Optimal approximate conversions of odds ratios and hazard ratios to risk ratios. Biometrics, 2019, (1): 1-7.

1. Austin PC. Absolute risk reductions, relative risks, relative risk reductions, and numbers needed to treat can be obtained from a logistic regression model. J Clin Epidemiol, 2010, 63(1): 2-6.
2. Zhang J, Yu KF. What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA, 1998, 280(19): 1690-1691.
3. 贺腾, 刘黎明, 曹汝, 等. 多靶点酪氨酸激酶抑制剂联合化疗与单纯化疗比较治疗晚期非小细胞肺癌疗效和安全性的 Meta 分析. 中国循证医学杂志, 2017, 17(10): 1198-1204.
4. 黄桥, 邝心颖, 童铁军, 等. 基于中位数为效应量的 meta 分析及 R 实现. 医学新知, 2020, 30(4): 265-271.
5. Saczynski JS, Spencer FA, Gore JM, et al. Twenty-year trends in the incidence of stroke complicating acute myocardial infarction: Worcester Heart Attack Study. Arch Intern Med, 2008, 168(19): 2104-2110.
6. Austin PC. The performance of different propensity-score methods for estimating relative risks. J Clin Epidemiol, 2008, 61(6): 537-545.
7. 付志智, 邓革红, 蔡富文, 等. 应用 log-binomial 回归和 logistic 回归分析法定传染病报告质量影响因素. 疾病监测, 2017, 32(9): 768-773.
8. 高文龙, 刘小宁, 颜虹. Log-binomial 回归在社区干预效果评价中的应用. 中国卫生统计, 2014, 31(3): 486-487, 491.
9. Deddens JA, Petersen MR, Lei X. Estimation of prevalence ratios when PROC GENMOD does not converge. SAS User Group International Proceedings Paper, 2003: 270-280.
10. Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol, 2004, 159(7): 702-706.
11. Austin PC. Absolute risk reductions and numbers needed to treat can be obtained from adjusted survival models for time-to-event outcomes. J Clin Epidemiol, 2010, 63(1): 46-55.
12. Breslow N. Covariance analysis of censored survival data. Biometrics, 1974, 30(1): 89-99.
13. 郜艳晖, 周舒冬, 李丽霞, 等. 基于相对危险度/患病率比的模型及参数估计方法研究进展. 中华流行病学杂志, 2013, 34(9): 935-939.
14. Fang J. Using SAS® procedures FREQ, GENMOD, LOGISTIC and PHREG to estimate adjusted relative risks-a case study. SAS Global Forum, 2011: 345.
15. Shor E, Roelfs D, Vang ZM. The "Hispanic mortality paradox" revisited: Meta-analysis and meta-regression of life-course differentials in Latin American and Caribbean immigrants' mortality. Soc Sci Med, 2017, 186: 20-33.
16. VanderWeele TJ. On a Square-Root transformation of the odds ratio for a common outcome. Epidemiology, 2017, 28(6): e58-e60.
17. VanderWeele TJ. Optimal approximate conversions of odds ratios and hazard ratios to risk ratios. Biometrics, 2019, (1): 1-7.

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