Literature DB >> 27748683

Biases in Randomized Trials: A Conversation Between Trialists and Epidemiologists.

Mohammad Ali Mansournia1, Julian P T Higgins, Jonathan A C Sterne, Miguel A Hernán.   

Abstract

Trialists and epidemiologists often employ different terminology to refer to biases in randomized trials and observational studies, even though many biases have a similar structure in both types of study. We use causal diagrams to represent the structure of biases, as described by Cochrane for randomized trials, and provide a translation to the usual epidemiologic terms of confounding, selection bias, and measurement bias. This structural approach clarifies that an explicit description of the inferential goal-the intention-to-treat effect or the per-protocol effect-is necessary to assess risk of bias in the estimates. Being aware of each other's terminologies will enhance communication between trialists and epidemiologists when considering key concepts and methods for causal inference.

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Year:  2017        PMID: 27748683      PMCID: PMC5130591          DOI: 10.1097/EDE.0000000000000564

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  18 in total

1.  Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis.

Authors:  E W Steyerberg; M J Eijkemans; J D Habbema
Journal:  J Clin Epidemiol       Date:  1999-10       Impact factor: 6.437

2.  Ignorability and bias in clinical trials.

Authors:  D F Heitjan
Journal:  Stat Med       Date:  1999 Sep 15-30       Impact factor: 2.373

3.  Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.

Authors:  Miguel A Hernán; Sonia Hernández-Díaz; Martha M Werler; Allen A Mitchell
Journal:  Am J Epidemiol       Date:  2002-01-15       Impact factor: 4.897

4.  Fallibility in estimating direct effects.

Authors:  Stephen R Cole; Miguel A Hernán
Journal:  Int J Epidemiol       Date:  2002-02       Impact factor: 7.196

5.  Instruments for causal inference: an epidemiologist's dream?

Authors:  Miguel A Hernán; James M Robins
Journal:  Epidemiology       Date:  2006-07       Impact factor: 4.822

6.  Matched designs and causal diagrams.

Authors:  Mohammad A Mansournia; Miguel A Hernán; Sander Greenland
Journal:  Int J Epidemiol       Date:  2013-06       Impact factor: 7.196

7.  Invited Commentary: Causal diagrams and measurement bias.

Authors:  Miguel A Hernán; Stephen R Cole
Journal:  Am J Epidemiol       Date:  2009-09-15       Impact factor: 4.897

8.  Structural Approach to Bias in Meta-analyses.

Authors:  Ian Shrier
Journal:  Res Synth Methods       Date:  2012-02-02       Impact factor: 5.273

9.  Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness.

Authors:  Sander Greenland; Mohammad Ali Mansournia
Journal:  Eur J Epidemiol       Date:  2015-02-17       Impact factor: 8.082

10.  Beyond the intention-to-treat in comparative effectiveness research.

Authors:  Miguel A Hernán; Sonia Hernández-Díaz
Journal:  Clin Trials       Date:  2011-09-23       Impact factor: 2.486

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  52 in total

1.  The Authors Respond.

Authors:  Mohammad Ali Mansournia; Miguel A Hernán
Journal:  Epidemiology       Date:  2017-07       Impact factor: 4.822

2.  For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates.

Authors:  Sander Greenland
Journal:  Eur J Epidemiol       Date:  2017-02-20       Impact factor: 8.082

3.  Effects of a responsible retailing mystery shop intervention on age verification by servers and clerks in alcohol outlets: A cluster randomised cross-over trial.

Authors:  Joel W Grube; William DeJong; Maureen DeJong; Sharon Lipperman-Kreda; Brad S Krevor
Journal:  Drug Alcohol Rev       Date:  2018-07-09

4.  Long-chain polyunsaturated fatty acids, gestation duration, and birth size: a Mendelian randomization study using fatty acid desaturase variants.

Authors:  Jonathan Y Bernard; Hong Pan; Izzuddin M Aris; Margarita Moreno-Betancur; Shu-E Soh; Fabian Yap; Kok Hian Tan; Lynette P Shek; Yap-Seng Chong; Peter D Gluckman; Philip C Calder; Keith M Godfrey; Mary Foong-Fong Chong; Michael S Kramer; Neerja Karnani; Yung Seng Lee
Journal:  Am J Clin Nutr       Date:  2018-07-01       Impact factor: 7.045

Review 5.  Considerations for Pharmacoepidemiological Studies of Drug-Cancer Associations.

Authors:  Anton Pottegård; Søren Friis; Til Stürmer; Jesper Hallas; Shahram Bahmanyar
Journal:  Basic Clin Pharmacol Toxicol       Date:  2018-01-15       Impact factor: 4.080

6.  Chitosan Use in Dentistry: A Systematic Review of Recent Clinical Studies.

Authors:  Marco Cicciù; Luca Fiorillo; Gabriele Cervino
Journal:  Mar Drugs       Date:  2019-07-17       Impact factor: 5.118

7.  Accounting for Time-Varying Confounding in the Relationship Between Obesity and Coronary Heart Disease: Analysis With G-Estimation: The ARIC Study.

Authors:  Maryam Shakiba; Mohammad Ali Mansournia; Arsalan Salari; Hamid Soori; Nasrin Mansournia; Jay S Kaufman
Journal:  Am J Epidemiol       Date:  2018-06-01       Impact factor: 4.897

Review 8.  What Can We Learn About Drug Safety and Other Effects in the Era of Electronic Health Records and Big Data That We Would Not Be Able to Learn From Classic Epidemiology?

Authors:  Ali Zarrinpar; Ting-Yuan David Cheng; Zhiguang Huo
Journal:  J Surg Res       Date:  2019-10-22       Impact factor: 2.192

9.  Effect of Smoking on Breast Cancer by Adjusting for Smoking Misclassification Bias and Confounders Using a Probabilistic Bias Analysis Method.

Authors:  Reza Pakzad; Saharnaz Nedjat; Mehdi Yaseri; Hamid Salehiniya; Nasrin Mansournia; Maryam Nazemipour; Mohammad Ali Mansournia
Journal:  Clin Epidemiol       Date:  2020-05-28       Impact factor: 4.790

10.  Tutorial on directed acyclic graphs.

Authors:  Jean C Digitale; Jeffrey N Martin; Medellena Maria Glymour
Journal:  J Clin Epidemiol       Date:  2021-08-08       Impact factor: 6.437

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