Recognizing and avoiding pitfalls and bias in database research - 15/11/25
Doi : 10.1016/j.jaad.2025.09.107
Maria C. Schneeweiss, MD a, b, ⁎ 
, Arash Mostaghimi, MD, MPA, MPH a, b, c, Sebastian Schneeweiss, MD, ScD a, b
, Arash Mostaghimi, MD, MPA, MPH a, b, c, Sebastian Schneeweiss, MD, ScD a, b a Dermato-Pharmacoepidemiology Work Group, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
b Harvard Medical School, Boston, Massachusetts
c Department of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts
∗Correspondence to: Maria C. Schneeweiss, MD, Dermato-Pharmacoepidemiology Program, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1 Brigham Circle (Suite 3030), Boston, MA 02120. www.bwhepidermis.orgDermato-Pharmacoepidemiology ProgramDivision of Pharmacoepidemiology and PharmacoeconomicsDepartment of MedicineBrigham and Women's Hospital and Harvard Medical School1 Brigham Circle (Suite 3030)BostonMA02120
Sous presse. Épreuves corrigées par l'auteur.
Disponible en ligne depuis le Saturday 15 November 2025
Key words : bias, causal inference, database analyses, electronic health care data, epidemiology, study design
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Plan
| Funding sources: This work was funded by the grant R01-AR080194 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases. |
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| Patient consent: Not applicable. |
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| IRB approval status: The Brigham and Women's Hospital's institutional review board approved this study (#2021P002323). |
© 2025
Publié par Elsevier Masson SAS.
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