Literature DB >> 34260087

Prevalence of Avoidable and Bias-Inflicting Methodological Pitfalls in Real-World Studies of Medication Safety and Effectiveness.

Katsiaryna Bykov1, Elisabetta Patorno1, Elvira D'Andrea1, Mengdong He1, Hemin Lee1, Jennifer S Graff2, Jessica M Franklin1.   

Abstract

Many real-word evidence (RWE) studies that utilize existing healthcare data to evaluate treatment effects incur substantial but avoidable bias from methodologically flawed study design; however, the extent of preventable methodological pitfalls in current RWE is unknown. To characterize the prevalence of avoidable methodological pitfalls with potential for bias in published claims-based studies of medication safety or effectiveness, we conducted an English-language search of PubMed for articles published from January 1, 2010 to May 20, 2019 and randomly selected 75 studies (10 case-control and 65 cohort studies) that evaluated safety or effectiveness of cardiovascular, diabetes, or osteoporosis medications using US health insurance claims. General and methodological study characteristics were extracted independently by two reviewers, and potential for bias was assessed across nine bias domains. Nearly all studies (95%) had at least one avoidable methodological issue known to incur bias, and 81% had potentially at least one of the four issues considered major due to their potential to undermine study validity: time-related bias (57%), potential for depletion of outcome-susceptible individuals (44%), inappropriate adjustment for postbaseline variables (41%), or potential for reverse causation (39%). The median number of major issues per study was 2 (interquartile range (IQR), 1-3) and was lower in cohort studies with a new-user, active-comparator design (median 1, IQR 0-1) than in cohort studies of prevalent users with a nonuser comparator (median 3, IQR 3-4). Recognizing and avoiding known methodological study design pitfalls could substantially improve the utility of RWE and confidence in its validity.
© 2021 The Authors. Clinical Pharmacology & Therapeutics © 2021 American Society for Clinical Pharmacology and Therapeutics.

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Year:  2021        PMID: 34260087      PMCID: PMC8678198          DOI: 10.1002/cpt.2364

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  33 in total

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Authors:  Linda E Lévesque; James A Hanley; Abbas Kezouh; Samy Suissa
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2.  Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

Authors:  Miguel A Hernán; James M Robins
Journal:  Am J Epidemiol       Date:  2016-03-18       Impact factor: 4.897

3.  Design and analysis choices for safety surveillance evaluations need to be tuned to the specifics of the hypothesized drug-outcome association.

Authors:  Susan Gruber; Aloka Chakravarty; Susan R Heckbert; Mark Levenson; David Martin; Jennifer C Nelson; Bruce M Psaty; Simone Pinheiro; Christian G Reich; Sengwee Toh; Alexander M Walker
Journal:  Pharmacoepidemiol Drug Saf       Date:  2016-07-14       Impact factor: 2.890

4.  When and How Can Real World Data Analyses Substitute for Randomized Controlled Trials?

Authors:  Jessica M Franklin; Sebastian Schneeweiss
Journal:  Clin Pharmacol Ther       Date:  2017-09-25       Impact factor: 6.875

Review 5.  Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses.

Authors:  Miguel A Hernán; Brian C Sauer; Sonia Hernández-Díaz; Robert Platt; Ian Shrier
Journal:  J Clin Epidemiol       Date:  2016-05-27       Impact factor: 6.437

6.  Avoidable flaws in observational analyses: an application to statins and cancer.

Authors:  Barbra A Dickerman; Xabier García-Albéniz; Roger W Logan; Spiros Denaxas; Miguel A Hernán
Journal:  Nat Med       Date:  2019-10-07       Impact factor: 53.440

7.  Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease.

Authors:  Miguel A Hernán; Alvaro Alonso; Roger Logan; Francine Grodstein; Karin B Michels; Walter C Willett; Joann E Manson; James M Robins
Journal:  Epidemiology       Date:  2008-11       Impact factor: 4.822

8.  Evaluating medication effects outside of clinical trials: new-user designs.

Authors:  Wayne A Ray
Journal:  Am J Epidemiol       Date:  2003-11-01       Impact factor: 4.897

9.  Time-related biases in pharmacoepidemiology.

Authors:  Samy Suissa; Sophie Dell'Aniello
Journal:  Pharmacoepidemiol Drug Saf       Date:  2020-08-11       Impact factor: 2.890

10.  Randomised controlled trials and population-based observational research: partners in the evolution of medical evidence.

Authors:  C M Booth; I F Tannock
Journal:  Br J Cancer       Date:  2014-01-14       Impact factor: 7.640

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  1 in total

1.  Real-world data: Assessing electronic health records and medical claims data to support regulatory decision-making for drug and biological products.

Authors:  Cynthia J Girman; Mary E Ritchey; Vincent Lo Re
Journal:  Pharmacoepidemiol Drug Saf       Date:  2022-05-03       Impact factor: 2.732

  1 in total

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