Literature DB >> 35120299

Beyond Confounding: Identifying Selection Bias in Observational Pulmonary and Critical Care Research.

Andrew J Admon1,2,3, Amy S B Bohnert4,2,5,6, Colin R Cooke1,2, Stephanie Parks Taylor7,8,9.   

Abstract

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Year:  2022        PMID： 35120299      PMCID： PMC9278626          DOI： 10.1513/AnnalsATS.202110-1188PS

Source DB:  PubMed          Journal:  Ann Am Thorac Soc        ISSN： 2325-6621


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  21 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.  A structural approach to selection bias.

Authors:  Miguel A Hernán; Sonia Hernández-Díaz; James M Robins
Journal:  Epidemiology       Date:  2004-09       Impact factor: 4.822

3.  Robust causal inference using directed acyclic graphs: the R package 'dagitty'.

Authors:  Johannes Textor; Benito van der Zander; Mark S Gilthorpe; Maciej Liskiewicz; George Th Ellison
Journal:  Int J Epidemiol       Date:  2016-12-01       Impact factor: 7.196

4.  Can We Trust Observational Studies Using Propensity Scores in the Critical Care Literature? A Systematic Comparison With Randomized Clinical Trials.

Authors:  Georgios D Kitsios; Issa J Dahabreh; Sean Callahan; Jessica K Paulus; Anthony C Campagna; James M Dargin
Journal:  Crit Care Med       Date:  2015-09       Impact factor: 7.598

5.  Invited Commentary: Selection Bias Without Colliders.

Authors:  Miguel A Hernán
Journal:  Am J Epidemiol       Date:  2017-06-01       Impact factor: 4.897

6.  Observational Research for Therapies Titrated to Effect and Associated With Severity of Illness: Misleading Results From Commonly Used Statistical Methods.

Authors:  Harm-Jan de Grooth; Armand R J Girbes; Fleur van der Ven; Heleen M Oudemans-van Straaten; Pieter R Tuinman; Angélique M E de Man
Journal:  Crit Care Med       Date:  2020-12       Impact factor: 7.598

7.  Timing Is Everything. The Importance of Alignment of Time Anchors for Observational Causal Inference Research.

Authors:  Stephanie Parks Taylor; Marc A Kowalkowski; Andrew J Admon
Journal:  Ann Am Thorac Soc       Date:  2021-05

8.  Emulating a Novel Clinical Trial Using Existing Observational Data. Predicting Results of the PreVent Study.

Authors:  Andrew J Admon; John P Donnelly; Jonathan D Casey; David R Janz; Derek W Russell; Aaron M Joffe; Derek J Vonderhaar; Kevin M Dischert; Susan B Stempek; James M Dargin; Todd W Rice; Theodore J Iwashyna; Matthew W Semler
Journal:  Ann Am Thorac Soc       Date:  2019-08

9.  Selection bias: a missing factor in the obesity paradox debate.

Authors:  Whitney R Robinson; Helena Furberg; Hailey R Banack
Journal:  Obesity (Silver Spring)       Date:  2014-03       Impact factor: 5.002

10.  Inverse-probability weighting and multiple imputation for evaluating selection bias in the estimation of childhood obesity prevalence using data from electronic health records.

Authors:  Carmen Sayon-Orea; Conchi Moreno-Iribas; Josu Delfrade; Manuela Sanchez-Echenique; Pilar Amiano; Eva Ardanaz; Javier Gorricho; Garbiñe Basterra; Marian Nuin; Marcela Guevara
Journal:  BMC Med Inform Decis Mak       Date:  2020-01-20       Impact factor: 2.796
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  1 in total

1.  Association of Unit Census with Delays in Antimicrobial Initiation among Ward Patients with Hospital-acquired Sepsis.

Authors:  Jennifer C Ginestra; Rachel Kohn; Rebecca A Hubbard; Andrew Crane-Droesch; Scott D Halpern; Meeta Prasad Kerlin; Gary E Weissman
Journal:  Ann Am Thorac Soc       Date:  2022-09
  1 in total

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