Deriving Real-World Insights From Real-World Data: Biostatistics to the Rescue
Authors: Michael J. Pencina, Frank W. Rockhold, Ralph B. D'Agostino
Journal: Annals of Internal Medicine (2018)

Ideas and Opinions18 September 2018Deriving Real-World Insights From Real-World Data: Biostatistics to the RescueMichael J. Pencina, PhD, Frank W. Rockhold, PhD, and Ralph B. D'Agostino Sr., PhDMichael J. Pencina, PhDDuke University School of Medicine, Durham, North Carolina (M.J.P.)Search for more papers by this author, Frank W. Rockhold, PhDDuke Clinical Research Institute, Durham, North Carolina (F.W.R.)Search for more papers by this author, and Ralph B. D'Agostino Sr., PhDBoston University, Boston, Massachusetts (R.B.D.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M18-1093 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Randomized controlled trials (RCTs) are widely considered the gold standard for inference on the comparative effects of 2 or more treatment strategies. Randomization is the most powerful technique available in causal inference, and the controlled setting of carefully designed experiments is a necessity in evaluating most treatments. Still, as currently designed, conducted, and analyzed, clinical trials are not without limitations and might be susceptible to some of the biases present in observational studies (1, 2). Strict inclusion and exclusion criteria, as well as trials often being conducted “in parallel” with regular clinical care, have led to concerns that the enrolled ...References1. Hernán MA, Hernández-Díaz S, Robins JM. Randomized trials analyzed as observational studies. Ann Intern Med. 2013;159:560-2. [PMID: 24018844] AbstractGoogle Scholar2. Mansournia MA, Higgins JP, Sterne JA, Hernán MA. Biases in randomized trials: a conversation between trialists and epidemiologists. Epidemiology. 2017;28:54-9. [PMID: 27748683] CrossrefMedlineGoogle Scholar3. Goldstein BA, Bhavsar NA, Phelan M, Pencina MJ. Controlling for informed presence bias due to the number of health encounters in an electronic health record. Am J Epidemiol. 2016;184:847-55. [PMID: 27852603] CrossrefMedlineGoogle Scholar4. Sherman RE, Anderson SA, DalPan GJ, Gray GW, Gross T, Hunter NL, et al. Real-world evidence—what is it and what can it tell us? N Engl J Med. 2016;375:2293-7. [PMID: 27959688] CrossrefMedlineGoogle Scholar5. Lauer MS, D'Agostino RB. The randomized registry trial—the next disruptive technology in clinical research? N Engl J Med. 2013;369:1579-81. [PMID: 23991657] doi:10.1056/NEJMp1310102 CrossrefMedlineGoogle Scholar6. Jones WS, Roe MT, Antman EM, Pletcher MJ, Harrington RA, Rothman RL, et al. The changing landscape of randomized clinical trials in cardiovascular disease. J Am Coll Cardiol. 2016;68:1898-1907. [PMID: 27765193] doi:10.1016/j.jacc.2016.07.781 CrossrefMedlineGoogle Scholar7. Pencina MJ, Peterson ED. Moving from clinical trials to precision medicine: the role for predictive modeling [Editorial]. JAMA. 2016;315:1713-4. [PMID: 27115375] doi:10.1001/jama.2016.4839 CrossrefMedlineGoogle Scholar8. Mansournia MA, Etminan M, Danaei G, Kaufman JS, Collins G. Handling time varying confounding in observational research. BMJ. 2017;359:j4587. [PMID: 29038130] doi:10.1136/bmj.j4587 CrossrefMedlineGoogle Scholar9. Lopes RD, Rordorf R, DeFerrari GM, Leonardi S, Thomas L, Wojdyla DM, et al; ARISTOTLE Committees and Investigators. Digoxin and mortality in patients with atrial fibrillation. J Am Coll Cardiol. 2018;71:1063-74. [PMID: 29519345] doi:10.1016/j.jacc.2017.12.060 CrossrefMedlineGoogle Scholar10. Li F, Morgan KL, Zaslavsky AM. Balancing covariates via propensity score weighting. J Am Stat Assoc. 2017;113:390-400. CrossrefGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Duke University School of Medicine, Durham, North Carolina (M.J.P.)Duke Clinical Research Institute, Durham, North Carolina (F.W.R.)Boston University, Boston, Massachusetts (R.B.D.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-1093.Corresponding Author: Michael J. Pencina, PhD, Duke University School of Medicine, 2400 Pratt Street, Duke Box 3850, Durham, NC 27710; e-mail, michael.[email protected]edu.Current Author Addresses: Dr. Pencina: Duke University School of Medicine, 2400 Pratt Street, Duke Box 3850, Durham, NC 27710.Dr. Rockhold: Duke Clinical Research Institute, 2400 Pratt Street, Duke Box 3850, Durham, NC 27710.Dr. D'Agostino: Boston University, 111 Cummington Mall, Boston, MA 02215.Author Contributions: Conception and design: M.J. Pencina, F.W. Rockhold, R.B. D'Agostino.Analysis and interpretation of the data: M.J. Pencina, F.W. Rockhold, R.B. D'Agostino.Drafting of the article: M.J. Pencina.Critical revision for important intellectual content: F.W. Rockhold, R.B. D'Agostino.Final approval of the article: M.J. Pencina, F.W. Rockhold, R.B. D'Agostino.Statistical expertise: M.J. Pencina, F.W. Rockhold, R.B. D'Agostino.This article was published at Annals.org on 24 July 2018. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoDeriving Real-World Insights From Real-World Data Stuart G. 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Baker, ScD 18 September 2018Volume 169, Issue 6Page: 401-402KeywordsAspirinAtrial fibrillationClinical trialsDisclosureElectronic medical recordsMortalityObservational studiesReproducibilityStatistical data ePublished: 24 July 2018 Issue Published: 18 September 2018 Copyright & PermissionsCopyright © 2018 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...