Literature DB >> 11338312

Data, design, and background knowledge in etiologic inference.

J M Robins1.   

Abstract

I use two examples to demonstrate that an appropriate etiologic analysis of an epidemiologic study depends as much on study design and background subject-matter knowledge as on the data. The demonstration is facilitated by the use of causal graphs.

Mesh：

Year:  2001        PMID： 11338312     DOI： 10.1097/00001648-200105000-00011

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


  89 in total

Review 1.  An introduction to causal inference.

Authors:  Judea Pearl
Journal:  Int J Biostat       Date:  2010-02-26       Impact factor: 0.968

2.  Menopausal hormone therapy and risks of colorectal adenomas and cancers in the French E3N prospective cohort: true associations or bias?

Authors:  Sophie Morois; Agnès Fournier; Françoise Clavel-Chapelon; Sylvie Mesrine; Marie-Christine Boutron-Ruault
Journal:  Eur J Epidemiol       Date:  2012-05-29       Impact factor: 8.082

3.  Imputation approaches for potential outcomes in causal inference.

Authors:  Daniel Westreich; Jessie K Edwards; Stephen R Cole; Robert W Platt; Sunni L Mumford; Enrique F Schisterman
Journal:  Int J Epidemiol       Date:  2015-07-25       Impact factor: 7.196

4.  Complex causal process diagrams for analyzing the health impacts of policy interventions.

Authors:  Michael Joffe; Jennifer Mindell
Journal:  Am J Public Health       Date:  2006-01-31       Impact factor: 9.308

5.  Model Misspecification When Excluding Instrumental Variables From PS Models in Settings Where Instruments Modify the Effects of Covariates on Treatment.

Authors:  Richard Wyss; Alan R Ellis; Mark Lunt; M Alan Brookhart; Robert J Glynn; Til Stürmer
Journal:  Epidemiol Methods       Date:  2014-12

6.  Results on differential and dependent measurement error of the exposure and the outcome using signed directed acyclic graphs.

Authors:  Tyler J VanderWeele; Miguel A Hernán
Journal:  Am J Epidemiol       Date:  2012-05-08       Impact factor: 4.897

7.  Confounding control in healthcare database research: challenges and potential approaches.

Authors:  M Alan Brookhart; Til Stürmer; Robert J Glynn; Jeremy Rassen; Sebastian Schneeweiss
Journal:  Med Care       Date:  2010-06       Impact factor: 2.983

8.  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

9.  Time-modified confounding.

Authors:  Robert W Platt; Enrique F Schisterman; Stephen R Cole
Journal:  Am J Epidemiol       Date:  2009-08-12       Impact factor: 4.897

10.  Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs.

Authors:  T Stürmer; R Wyss; R J Glynn; M A Brookhart
Journal:  J Intern Med       Date:  2014-02-13       Impact factor: 8.989
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