Literature DB >> 29372462

Bias in matched case-control studies: DAGs are not enough.

Neil Pearce1.   

Abstract

Mesh：

Year:  2018        PMID： 29372462     DOI： 10.1007/s10654-018-0362-3

Source DB:  PubMed          Journal:  Eur J Epidemiol        ISSN： 0393-2990            Impact factor:   8.082


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  14 in total

1.  Quantifying biases in causal models: classical confounding vs collider-stratification bias.

Authors:  Sander Greenland
Journal:  Epidemiology       Date:  2003-05       Impact factor: 4.822

2.  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

3.  Illustrating bias due to conditioning on a collider.

Authors:  Stephen R Cole; Robert W Platt; Enrique F Schisterman; Haitao Chu; Daniel Westreich; David Richardson; Charles Poole
Journal:  Int J Epidemiol       Date:  2009-11-19       Impact factor: 7.196

4.  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

5.  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

6.  Commentary: Three worlds collide: Berkson's bias, selection bias and collider bias.

Authors:  Neil Pearce; Lorenzo Richiardi
Journal:  Int J Epidemiol       Date:  2014-02-28       Impact factor: 7.196

7.  Case-control matching: effects, misconceptions, and recommendations.

Authors:  Mohammad Ali Mansournia; Nicholas Patrick Jewell; Sander Greenland
Journal:  Eur J Epidemiol       Date:  2017-11-03       Impact factor: 12.434

8.  Causal inference-so much more than statistics.

Authors:  Neil Pearce; Debbie A Lawlor
Journal:  Int J Epidemiol       Date:  2016-12-01       Impact factor: 7.196

9.  Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms.

Authors:  O O Aalen; K Røysland; J M Gran; R Kouyos; T Lange
Journal:  Stat Methods Med Res       Date:  2014-01-23       Impact factor: 3.021

10.  Commentary: The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?

Authors:  Rhian M Daniel; Bianca L De Stavola; Stijn Vansteelandt
Journal:  Int J Epidemiol       Date:  2016-12-01       Impact factor: 9.685
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  5 in total

1.  A note of thanks and an invitation.

Authors:  Albert Hofman
Journal:  Eur J Epidemiol       Date:  2018-12       Impact factor: 8.082

2.  Individually-matched etiologic studies: classical estimators made new again.

Authors:  James A Hanley
Journal:  Eur J Epidemiol       Date:  2018-08-24       Impact factor: 8.082

3.  Outdoor particulate matter (PM10) exposure and lung cancer risk in the EAGLE study.

Authors:  Dario Consonni; Michele Carugno; Sara De Matteis; Francesco Nordio; Giorgia Randi; Martina Bazzano; Neil E Caporaso; Margaret A Tucker; Pier Alberto Bertazzi; Angela C Pesatori; Jay H Lubin; Maria Teresa Landi
Journal:  PLoS One       Date:  2018-09-14       Impact factor: 3.240

4.  Untargeted Metabolomics: Biochemical Perturbations in Golestan Cohort Study Opium Users Inform Intervention Strategies.

Authors:  Yuan-Yuan Li; Reza Ghanbari; Wimal Pathmasiri; Susan McRitchie; Hossein Poustchi; Amaneh Shayanrad; Gholamreza Roshandel; Arash Etemadi; Jonathan D Pollock; Reza Malekzadeh; Susan C J Sumner
Journal:  Front Nutr       Date:  2020-12-22

5.  Causal inference concepts applied to three observational studies in the context of vaccine development: from theory to practice.

Authors:  Emilia Gvozdenović; Lucio Malvisi; Elisa Cinconze; Stijn Vansteelandt; Phoebe Nakanwagi; Emmanuel Aris; Dominique Rosillon
Journal:  BMC Med Res Methodol       Date:  2021-02-15       Impact factor: 4.615
  5 in total

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