Sensitivity Analysis for Unmeasured Confounding: E-Values for Observational Studies

Editorials15 August 2017Sensitivity Analysis for Unmeasured Confounding: E-Values for Observational StudiesA. Russell Localio, PhD, Catherine B. Stack, PhD, and Michael E. Griswold, PhDA. Russell Localio, PhDFrom University of Pennsylvania, Philadelphia, Pennsylvania; American College of Physicians, Philadelphia, Pennsylvania; and University of Mississippi Medical Center, Jackson, Mississippi., Catherine B. Stack, PhDFrom University of Pennsylvania, Philadelphia, Pennsylvania; American College of Physicians, Philadelphia, Pennsylvania; and University of Mississippi Medical Center, Jackson, Mississippi., and Michael E. Griswold, PhDFrom University of Pennsylvania, Philadelphia, Pennsylvania; American College of Physicians, Philadelphia, Pennsylvania; and University of Mississippi Medical Center, Jackson, Mississippi.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M17-1485 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail In their current article in Annals, VanderWeele and Ding (1) introduce the "E-value" as a simple measure of the potential for bias arising from unmeasured confounders in observational studies. Bias often poses a greater threat to the validity of reported findings than does the random variability reflected by P values and confidence bounds (2). Although the potential for bias is widely known, reports of observational data often lack sensitivity analyses exploring the possible influence of bias from unobserved factors, perhaps because authors face challenges in specifying the elements and degree of possible confounding in a manner that readers can understand.... References1. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med. 2017;167:268-74. doi:10.7326/M16-2607 LinkGoogle Scholar2. Bailar JC, Mosteller F. Guidelines for statistical reporting in articles for medical journals. Amplifications and explanations. Ann Intern Med. 1988;108:266-73. [PMID: 3341656] LinkGoogle Scholar3. Park SY, Freedman ND, Haiman CA, LeMarchand L, Wilkens LR, Setiawan VW. Association of coffee consumption with total and cause-specific mortality among nonwhite populations. Ann Intern Med. 2017;167:228-35. doi:10.7326/M16-2472 LinkGoogle Scholar4. Rosenbaum PR. Heterogeneity and causality. Am Stat. 2005;59:147-52. CrossrefGoogle Scholar5. Keogh RH, White IR. A toolkit for measurement error correction, with a focus on nutritional epidemiology. Stat Med. 2014;33:2137-55. [PMID: 24497385] doi:10.1002/sim.6095 CrossrefMedlineGoogle Scholar6. Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999;8:3-15. [PMID: 10347857] CrossrefMedlineGoogle Scholar7. Daniels MJ, Hogan JW. Missing Data in Longitudinal Studies. Strategies for Bayesian Modeling and Sensitivity Analysis. Boca Raton, FL: Chapman & Hall; 2008. Google Scholar8. McCulloch CE, Neuhaus JM, Olin RL. Biased and unbiased estimation in longitudinal studies with informative visit processes. Biometrics. 2016;72:1315-1324. [PMID: 26990830] doi:10.1111/biom.12501 CrossrefMedlineGoogle Scholar9. Chatfield C. Model uncertainty, data mining and statistical inference. J R Stat Soc Ser A Stat Soc. 1995;158:419-66. CrossrefGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From University of Pennsylvania, Philadelphia, Pennsylvania; American College of Physicians, Philadelphia, Pennsylvania; and University of Mississippi Medical Center, Jackson, Mississippi.Acknowledgment: The authors thank the Annals senior clinical and statistical editors for their input and review of earlier drafts of the manuscript, and especially thank Eliseo Guallar, MD, DrPH.Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M17-1485.Corresponding Author: A. Russell Localio, PhD, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 635 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104; e-mail, [email protected]med.upenn.edu.Current Author Addresses: Dr. Localio: Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 635 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104.Dr. Stack: American College of Physicians, 190 N. Independence Mall West, Philadelphia, PA 19106.Dr. Griswold: Department of Data Science, University of Mississippi Medical Center, New Guyton Suite G651, 2500 North State Street, Jackson, MS 39216.This article was published at Annals.org on 11 July 2017. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoSensitivity Analysis in Observational Research: Introducing the E-Value Tyler J. 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Rich-Edwards, ScDAntithyroid Drugs and Congenital Malformations A Nationwide Korean Cohort StudyGi Hyeon Seo, MD, Tae Hyuk Kim, MD, PhD, and Jae Hoon Chung, MD, PhDModerate Coffee Intake Can Be Part of a Healthy DietEliseo Guallar, MD, DrPH, Elena Blasco-Colmenares, MD, PhD, MPH, Dan E. Arking, PhD, and Di Zhao, PhD 15 August 2017Volume 167, Issue 4Page: 285-286KeywordsBiostatisticsCoffeeEpidemiologyHazard ratioMortalityObservational studiesRelative riskResearch and reporting methodsResearch designRisk ratio ePublished: 11 July 2017 Issue Published: 15 August 2017 Copyright & PermissionsCopyright © 2017 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...