Effect Estimates in Randomized Trials and Observational Studies: Comparing Apples With Apples

Author:

Lodi Sara1,Phillips Andrew2,Lundgren Jens3ORCID,Logan Roger4,Sharma Shweta5,Cole Stephen R,Babiker Abdel6,Law Matthew7,Chu Haitao5,Byrne Dana8,Horban Andrzej9,Sterne Jonathan A C1011,Porter Kholoud2,Sabin Caroline2,Costagliola Dominique12,Abgrall Sophie1213,Gill John1415,Touloumi Giota16,Pacheco Antonio G17,van Sighem Ard18,Reiss Peter181920,Bucher Heiner C21,Montoliu Giménez Alexandra22,Jarrin Inmaculada23,Wittkop Linda24,Meyer Laurence2526,Perez-Hoyos Santiago27,Justice Amy28,Neaton James D5,Hernán Miguel A42930,

Affiliation:

1. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts

2. Institute for Global Health, University College London, United Kingdom

3. Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, Denmark

4. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

5. Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota

6. Medical Research Council, Clinical Trials Unit in University College London, London, United Kingdom

7. The Kirby Institute, Sidney, Australia

8. Division of Infectious Diseases, Department of Medicine, Cooper University Hospital, Cooper Medical School at Rowan University, New Jersey

9. Medical University of Warsaw, Department for Adult's Infectious Diseases, Warsaw, Poland

10. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, North Carolina

11. Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom

12. INSERM, Sorbonne Université, Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Paris, France

13. AP-HP, Hôpital Antoine Béclère, Service de Médecine Interne, Clamart, France

14. Southern Alberta Clinic, Calgary, Canada

15. Department of Medicine, University of Calgary, Canada

16. National and Kapodistrian University of Athens, Faculty of Medicine, Dept. of Hygiene, Epidemiology and Medical Statistics, Greece

17. Programa de Computação Científica, Fundacao Oswaldo Cruz, Rio de Janeiro, Brasil

18. Stichting HIV Monitoring, Amsterdam, the Netherlands

19. Amsterdam University Medical Centres, University of Amsterdam, Department of Global Health and Division of Infectious Diseases, Amsterdam, the Netherlands

20. Amsterdam Institute for Global Health and Development, and Amsterdam Public Health Research Institute, Amsterdam, the Netherlands

21. Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, University of Basel, Switzerland

22. Centre for Epidemiological Studies on HIV/STI in Catalonia (CEEISCAT), Agència de Salut Pública de Catalunya (ASPC), Badalona, Spain

23. Centro Nacional de Epidemiología, Instituto de Salud Carlos III, Madrid, Spain

24. Univ. Bordeaux, ISPED, Inserm, Bordeaux Population Health Research Center, team MORPH3EUS, UMR 1219, CIC-EC 1401, Bordeaux, France

25. CHU de Bordeaux, Pôle de santé publique, Service d'information médicale, Bordeaux, France

26. Université Paris Sud, UMR 1018, le Kremlin Bicêtre, France

27. Vall d'Hebrón Research Institute; Barcelona, Spain

28. Yale University School of Medicine, New Haven, Connecticut

29. Department of Biostatistics, Harvard T.H. Chan School of Public Health

30. Harvard-MIT Division of Health Sciences and Technology, Boston, Massachusetts

Abstract

Abstract Effect estimates from randomized trials and observational studies might not be directly comparable because of differences in study design, other than randomization, and in data analysis. We propose a 3-step procedure to facilitate meaningful comparisons of effect estimates from randomized trials and observational studies: 1) harmonization of the study protocols (eligibility criteria, treatment strategies, outcome, start and end of follow-up, causal contrast) so that the studies target the same causal effect, 2) harmonization of the data analysis to estimate the causal effect, and 3) sensitivity analyses to investigate the impact of discrepancies that could not be accounted for in the harmonization process. To illustrate our approach, we compared estimates of the effect of immediate with deferred initiation of antiretroviral therapy in individuals positive for the human immunodeficiency virus from the Strategic Timing of Antiretroviral Therapy (START) randomized trial and the observational HIV-CAUSAL Collaboration.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Epidemiology

Reference47 articles.

1. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease;Hernán;Epidemiology,2008

2. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses;Hernán;J Clin Epidemiol,2016

3. Per-protocol analyses of pragmatic trials;Hernán;N Engl J Med,2017

4. Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials;Anglemyer;Cochrane Database Syst Rev,2014

5. A comparison of observational studies and randomized, controlled trials;Benson;N Engl J Med,2000

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3