Application progress and interpretation of E-value in sensitivity analysis_Chinese Journal of Evidence-Based Medicine

Authors：

FANG Hanyu ^1,2 , ZHANG Hongchun ^1,2 , HONG Zheng ^1,2 , LI Sheyu ^3,4 , LIU Jianping ^5,6 ,  ZHANG Ying ^5,6

1. Beijing University of Chinese Medicine, Beijing 100029, P. R. China;
2. Department of Traditional Chinese Medicine for Pulmonary Diseases, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, P. R. China;
3. Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
4. Chinese Evidence-based Medicine Center / MAGIC China Center / Cochrane China, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
5. School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, P. R. China;
6. International Institute of Evidence-based Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, P. R. China;

Corresponding author：

ZHANG Ying, Email: yingzhang@bucm.edu.cn

Keywords：

Observational study; E-value; Sensitivity analysis; Causal inference; Effect value

DOI：

10.7507/1672-2531.202202006

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective When making causal inferences in observational studies, in order to improve the robustness of the results of observational studies, statistical analysis techniques are often used to estimate the impact of unmeasured potential confounding factors. By systematically reviewing the application progress of the E-value, one of the sensitivity analysis methods, the advantages and limitations of using the E-value were discussed, to provide references for the application, reporting and interpretation of the E-value. Methods In the PubMed database, E-value was used as a keyword for title, abstract and key paper citation retrieval, and the literature that used the E-value analysis method for sensitivity analysis during 2016-2021 was screened. Results The E-value was widely used not only in cohort studies (n=215) and case-control studies (n=15), but also in cross-sectional studies (n=28), randomized controlled trials (n=6) and meta-analysis (n=16). The E-value was often combined with other sensitivity analysis methods, such as hierarchical analysis, instrumental variables, and multiple statistical regression models that correct different covariates, to further explore the reliability and robustness of the results. Conclusion When the E-value is used to evaluate the confounding factors in observational studies, the confidence interval and P value can be combined to evaluate the sensitivity of the results more comprehensively.

Citation： FANG Hanyu, ZHANG Hongchun, HONG Zheng, LI Sheyu, LIU Jianping, ZHANG Ying. Application progress and interpretation of E-value in sensitivity analysis. Chinese Journal of Evidence-Based Medicine, 2022, 22(8): 988-992. doi: 10.7507/1672-2531.202202006 Copy

1.	Haneuse S, VanderWeele TJ, Arterburn D. Using the E-value to assess the potential effect of unmeasured confounding in observational studies. JAMA, 2019, 321(6): 602-603.
2.	Ding P, Vanderweele TJ. Sensitivityanalysis without assumptions. Epidemiology, 2016, 27(3): 368-377.
3.	Streeter AJ, Lin NX, Crathorne L, et al. Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review. J Clin Epidemiol, 2017, 87: 23-34.
4.	姜棋予, 孙丽君, 毕京峰. 观察性研究的敏感性分析: “E-value”解读. 中国医药导报, 2020, 17(11): 181-185,192.
5.	Vale CCR, Almeida NKO, Almeida RMVR. On the use of the E-value for sensitivity analysis in epidemiologic studies. Cad Saude Publica, 2021, 37(6): e00294720.
6.	Nichols GA, Philip S, Reynolds K, et al. Increased residual cardiovascular risk in patients with diabetes and high versus normal triglycerides despite statin-controlled LDL cholesterol. Diabetes Obes Metab, 2019, 21(2): 366-371.
7.	Mathur MB, Ding P, Riddell CA, et al. Web site and R package for computing E-values. Epidemiology, 2018, 29(5): e45-e47.
8.	Fisher DP, Johnson E, Haneuse S, et al. Association between bariatric surgery and macrovascular disease outcomes in patients with type 2 diabetes and severe obesity. JAMA, 2018, 320(15): 1570-1582.
9.	Anderson TS, Jing B, Auerbach A, et al. Clinical outcomes after intensifying antihypertensive medication regimens among older adults at hospital discharge. JAMA Intern Med, 2019, 179(11): 1528-1536.
10.	Fuertes E, Carsin AE, Garcia-Larsen V, et al. The role of C-reactive protein levels on the association of physical activity with lung function in adults. PLoS One, 2019, 14(9): e0222578.
11.	Petridou ET, Georgakis MK, Erdmann F, et al. Advanced parental age as risk factor for childhood acute lymphoblastic leukemia: results from studies of the Childhood Leukemia International Consortium. Eur J Epidemiol, 2018, 33(10): 965-976.
12.	Marsden J, Goetz C, Meynen T, et al. Memory-focused cognitive therapy for cocaine use disorder: theory, procedures and preliminary evidence from an external pilot randomised controlled trial. EBioMedicine, 2018, 29: 177-189.
13.	Brookhart MA, Wang PS, Solomon DH, et al. Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable. Epidemiology, 2006, 17(3): 268-275.
14.	Chen Y, Briesacher BA. Use of instrumental variable in prescription drug research with observational data: a systematic review. J Clin Epidemiol, 2011, 64(6): 687-700.
15.	Davies NM, Smith GD, Windmeijer F, et al. Issues in the reporting and conduct of instrumental variable studies: a systematic review. Epidemiology, 2013, 24(3): 363-369.
16.	Ashenfelter O. Estimating the effect of training programs on earnings. Rev Econom Stat, 1976, 60(1): 47-57.
17.	Patel MS, Volpp KG, Small DS, et al. Association of the 2011 ACGME resident duty hour reforms with mortality and readmissions among hospitalized Medicare patients. JAMA, 2014, 312(22): 2364-2373.
18.	Rajaram R, Chung JW, Jones AT, et al. Association of the 2011 ACGME resident duty hour reform with general surgery patient outcomes and with resident examination performance. JAMA, 2014, 312(22): 2374-2384.
19.	Yu M, Xie D, Wang X, et al. Prior event rate ratio adjustment: numerical studies of a statistical method to address unrecognized confounding in observational studies. Pharmacoepidemiol Drug Saf, 2012, 21(Suppl 2): 60-68.
20.	Tannen RL, Weiner MG, Xie D. Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomised controlled trial findings. BMJ, 2009, 338: b81.
21.	Tannen R, Xie D, Wang X, et al. A new "Comparative Effectiveness" assessment strategy using the THIN database: comparison of the cardiac complications of pioglitazone and rosiglitazone. Pharmacoepidemiol Drug Saf, 2013, 22(1): 86-97.
22.	黄丽红, 魏永越, 陈峰. 如何控制观察性疗效比较研究中的混杂因素: (二)未知或未测量混杂因素的统计学分析方法. 中华流行病学杂志, 2019, 40(11): 1450-1455.
23.	Ioannidis JPA, Tan YJ, Blum MR. Limitations and misinterpretations of E-values for sensitivity analyses of observational studies. Ann Intern Med, 2019, 170(2): 108-111.
24.	vander Weele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med, 2017, 167(4): 268-274.
25.	Guyatt GH, Oxman AD, Sultan S, 等. GRADE指南: Ⅸ.证据质量升级. 中国循证医学杂志, 2011, 11(12): 1459-1463.

1. Haneuse S, VanderWeele TJ, Arterburn D. Using the E-value to assess the potential effect of unmeasured confounding in observational studies. JAMA, 2019, 321(6): 602-603.
2. Ding P, Vanderweele TJ. Sensitivityanalysis without assumptions. Epidemiology, 2016, 27(3): 368-377.
3. Streeter AJ, Lin NX, Crathorne L, et al. Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review. J Clin Epidemiol, 2017, 87: 23-34.
4. 姜棋予, 孙丽君, 毕京峰. 观察性研究的敏感性分析: “E-value”解读. 中国医药导报, 2020, 17(11): 181-185,192.
5. Vale CCR, Almeida NKO, Almeida RMVR. On the use of the E-value for sensitivity analysis in epidemiologic studies. Cad Saude Publica, 2021, 37(6): e00294720.
6. Nichols GA, Philip S, Reynolds K, et al. Increased residual cardiovascular risk in patients with diabetes and high versus normal triglycerides despite statin-controlled LDL cholesterol. Diabetes Obes Metab, 2019, 21(2): 366-371.
7. Mathur MB, Ding P, Riddell CA, et al. Web site and R package for computing E-values. Epidemiology, 2018, 29(5): e45-e47.
8. Fisher DP, Johnson E, Haneuse S, et al. Association between bariatric surgery and macrovascular disease outcomes in patients with type 2 diabetes and severe obesity. JAMA, 2018, 320(15): 1570-1582.
9. Anderson TS, Jing B, Auerbach A, et al. Clinical outcomes after intensifying antihypertensive medication regimens among older adults at hospital discharge. JAMA Intern Med, 2019, 179(11): 1528-1536.
10. Fuertes E, Carsin AE, Garcia-Larsen V, et al. The role of C-reactive protein levels on the association of physical activity with lung function in adults. PLoS One, 2019, 14(9): e0222578.
11. Petridou ET, Georgakis MK, Erdmann F, et al. Advanced parental age as risk factor for childhood acute lymphoblastic leukemia: results from studies of the Childhood Leukemia International Consortium. Eur J Epidemiol, 2018, 33(10): 965-976.
12. Marsden J, Goetz C, Meynen T, et al. Memory-focused cognitive therapy for cocaine use disorder: theory, procedures and preliminary evidence from an external pilot randomised controlled trial. EBioMedicine, 2018, 29: 177-189.
13. Brookhart MA, Wang PS, Solomon DH, et al. Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable. Epidemiology, 2006, 17(3): 268-275.
14. Chen Y, Briesacher BA. Use of instrumental variable in prescription drug research with observational data: a systematic review. J Clin Epidemiol, 2011, 64(6): 687-700.
15. Davies NM, Smith GD, Windmeijer F, et al. Issues in the reporting and conduct of instrumental variable studies: a systematic review. Epidemiology, 2013, 24(3): 363-369.
16. Ashenfelter O. Estimating the effect of training programs on earnings. Rev Econom Stat, 1976, 60(1): 47-57.
17. Patel MS, Volpp KG, Small DS, et al. Association of the 2011 ACGME resident duty hour reforms with mortality and readmissions among hospitalized Medicare patients. JAMA, 2014, 312(22): 2364-2373.
18. Rajaram R, Chung JW, Jones AT, et al. Association of the 2011 ACGME resident duty hour reform with general surgery patient outcomes and with resident examination performance. JAMA, 2014, 312(22): 2374-2384.
19. Yu M, Xie D, Wang X, et al. Prior event rate ratio adjustment: numerical studies of a statistical method to address unrecognized confounding in observational studies. Pharmacoepidemiol Drug Saf, 2012, 21(Suppl 2): 60-68.
20. Tannen RL, Weiner MG, Xie D. Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomised controlled trial findings. BMJ, 2009, 338: b81.
21. Tannen R, Xie D, Wang X, et al. A new "Comparative Effectiveness" assessment strategy using the THIN database: comparison of the cardiac complications of pioglitazone and rosiglitazone. Pharmacoepidemiol Drug Saf, 2013, 22(1): 86-97.
22. 黄丽红, 魏永越, 陈峰. 如何控制观察性疗效比较研究中的混杂因素: (二)未知或未测量混杂因素的统计学分析方法. 中华流行病学杂志, 2019, 40(11): 1450-1455.
23. Ioannidis JPA, Tan YJ, Blum MR. Limitations and misinterpretations of E-values for sensitivity analyses of observational studies. Ann Intern Med, 2019, 170(2): 108-111.
24. vander Weele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med, 2017, 167(4): 268-274.
25. Guyatt GH, Oxman AD, Sultan S, 等. GRADE指南: Ⅸ.证据质量升级. 中国循证医学杂志, 2011, 11(12): 1459-1463.

Previous Article
Interpretation of STROBE-MR: a statement for strengthening the reporting of observational studies in epidemiology using Mendelian randomization

Chinese Journal of Evidence-Based Medicine

Application progress and interpretation of E-value in sensitivity analysis

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Format

Content