Literature DB >> 20139125

Invited commentary: positivity in practice.

Daniel Westreich1, Stephen R Cole.   

Abstract

Positivity, or the experimental treatment assignment assumption, requires that there be both exposed and unexposed participants at every combination of the values of the observed confounders in the population under study. Positivity is essential for inference but is often overlooked in practice by epidemiologists. This issue of the Journal includes 2 articles featuring discussions related to positivity. Here the authors define positivity, distinguish between deterministic and random positivity, and discuss the 2 relevant papers in this issue. In addition, the commentators illustrate positivity in simple 2 x 2 tables, as well as detail some ways in which epidemiologists may examine their data for nonpositivity and deal with violations of positivity in practice.

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Year:  2010        PMID: 20139125      PMCID: PMC2877454          DOI: 10.1093/aje/kwp436

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  11 in total

1.  An application of model-fitting procedures for marginal structural models.

Authors:  Kathleen M Mortimer; Romain Neugebauer; Mark van der Laan; Ira B Tager
Journal:  Am J Epidemiol       Date:  2005-07-13       Impact factor: 4.897

2.  Estimating causal effects from epidemiological data.

Authors:  Miguel A Hernán; James M Robins
Journal:  J Epidemiol Community Health       Date:  2006-07       Impact factor: 3.710

3.  The consistency statement in causal inference: a definition or an assumption?

Authors:  Stephen R Cole; Constantine E Frangakis
Journal:  Epidemiology       Date:  2009-01       Impact factor: 4.822

4.  The association between persistent fetal occiput posterior position and perinatal outcomes: an example of propensity score and covariate distance matching.

Authors:  Yvonne W Cheng; Alan Hubbard; Aaron B Caughey; Ira B Tager
Journal:  Am J Epidemiol       Date:  2010-02-05       Impact factor: 4.897

5.  Exposure opportunity in epidemiologic studies.

Authors:  J J Schlesselman; B V Stadel
Journal:  Am J Epidemiol       Date:  1987-02       Impact factor: 4.897

6.  A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods.

Authors:  J Robins
Journal:  J Chronic Dis       Date:  1987

7.  The effectiveness of adjustment by subclassification in removing bias in observational studies.

Authors:  W G Cochran
Journal:  Biometrics       Date:  1968-06       Impact factor: 2.571

Review 8.  Critical appraisal of the exposure-potential restriction rule.

Authors:  C Poole
Journal:  Am J Epidemiol       Date:  1987-02       Impact factor: 4.897

9.  Exposure opportunity in case-control studies.

Authors:  C Poole
Journal:  Am J Epidemiol       Date:  1986-02       Impact factor: 4.897

10.  Constructing inverse probability weights for marginal structural models.

Authors:  Stephen R Cole; Miguel A Hernán
Journal:  Am J Epidemiol       Date:  2008-08-05       Impact factor: 4.897

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  139 in total

1.  The positivity assumption and marginal structural models: the example of warfarin use and risk of bleeding.

Authors:  Robert William Platt; Joseph Austin Christopher Delaney; Samy Suissa
Journal:  Eur J Epidemiol       Date:  2011-12-08       Impact factor: 8.082

2.  Marginal structural models for case-cohort study designs to estimate the association of antiretroviral therapy initiation with incident AIDS or death.

Authors:  Stephen R Cole; Michael G Hudgens; Phyllis C Tien; Kathryn Anastos; Lawrence Kingsley; Joan S Chmiel; Lisa P Jacobson
Journal:  Am J Epidemiol       Date:  2012-02-01       Impact factor: 4.897

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.  Approaches to treatment effect heterogeneity in the presence of confounding.

Authors:  Sarah C Anoke; Sharon-Lise Normand; Corwin M Zigler
Journal:  Stat Med       Date:  2019-03-31       Impact factor: 2.373

5.  Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling.

Authors:  Jonathan M Snowden; Colleen E Reid; Ira B Tager
Journal:  Epidemiology       Date:  2015-03       Impact factor: 4.822

6.  Estimating Human Immunodeficiency Virus (HIV) Prevention Effects in Low-incidence Settings.

Authors:  Jacqueline E Rudolph; Stephen R Cole; Joseph J Eron; Angela D Kashuba; Adaora A Adimora
Journal:  Epidemiology       Date:  2019-05       Impact factor: 4.822

7.  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

8.  Assessing mediation using marginal structural models in the presence of confounding and moderation.

Authors:  Donna L Coffman; Wei Zhong
Journal:  Psychol Methods       Date:  2012-08-20

9.  Medication, reperfusion therapy and survival in a community-based setting of hospitalised myocardial infarction.

Authors:  Emily C O'Brien; Kathryn M Rose; Chirayath M Suchindran; Til Stürmer; Patricia P Chang; Lloyd Chambless; Cameron S Guild; Wayne D Rosamond
Journal:  Heart       Date:  2013-03-02       Impact factor: 5.994

10.  Hip Fractures Risk in Older Men and Women Associated With DXA-Derived Measures of Thigh Subcutaneous Fat Thickness, Cross-Sectional Muscle Area, and Muscle Density.

Authors:  Serghei Malkov; Peggy M Cawthon; Kathy Wilt Peters; Jane A Cauley; Rachel A Murphy; Marjolein Visser; Joseph P Wilson; Tamara Harris; Suzanne Satterfield; Steve Cummings; John A Shepherd
Journal:  J Bone Miner Res       Date:  2015-05-21       Impact factor: 6.741

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