Literature DB >> 33336256

Commentary: Continuing the E-value's post-publication peer review.

Charles Poole.   

Abstract

Entities:  

Mesh：

Year:  2020        PMID： 33336256      PMCID： PMC7746397          DOI： 10.1093/ije/dyaa097

Source DB:  PubMed          Journal:  Int J Epidemiol        ISSN： 0300-5771            Impact factor:   9.685


× No keyword cloud information.
  11 in total

1.  Sensitivity Analysis for Unmeasured Confounding: E-Values for Observational Studies.

Authors:  A Russell Localio; Catherine B Stack; Michael E Griswold
Journal:  Ann Intern Med       Date:  2017-07-11       Impact factor: 25.391

2.  Sensitivity Analysis in Observational Research: Introducing the E-Value.

Authors:  Tyler J VanderWeele; Peng Ding
Journal:  Ann Intern Med       Date:  2017-07-11       Impact factor: 25.391

3.  Web Site and R Package for Computing E-values.

Authors:  Maya B Mathur; Peng Ding; Corinne A Riddell; Tyler J VanderWeele
Journal:  Epidemiology       Date:  2018-09       Impact factor: 4.822

4.  Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies.

Authors:  Sebastien Haneuse; Tyler J VanderWeele; David Arterburn
Journal:  JAMA       Date:  2019-02-12       Impact factor: 56.272

5.  Correcting Misinterpretations of the E-Value.

Authors:  Tyler J VanderWeele; Maya B Mathur; Peng Ding
Journal:  Ann Intern Med       Date:  2019-01-01       Impact factor: 25.391

6.  Limitations and Misinterpretations of E-Values for Sensitivity Analyses of Observational Studies.

Authors:  John P A Ioannidis; Yuan Jin Tan; Manuel R Blum
Journal:  Ann Intern Med       Date:  2019-01-01       Impact factor: 25.391

Review 7.  Mistake proofing: changing designs to reduce error.

Authors:  J R Grout
Journal:  Qual Saf Health Care       Date:  2006-12

8.  Invited commentary: evolution of epidemiologic evidence on magnetic fields and childhood cancers.

Authors:  C Poole
Journal:  Am J Epidemiol       Date:  1996-01-15       Impact factor: 4.897

9.  Use of E-values for addressing confounding in observational studies-an empirical assessment of the literature.

Authors:  Manuel R Blum; Yuan Jin Tan; John P A Ioannidis
Journal:  Int J Epidemiol       Date:  2020-10-01       Impact factor: 7.196

10.  Sensitivity Analysis Without Assumptions.

Authors:  Peng Ding; Tyler J VanderWeele
Journal:  Epidemiology       Date:  2016-05       Impact factor: 4.822
View more
  6 in total

1.  Neurodevelopmental Outcomes at 1 Year in Infants of Mothers Who Tested Positive for SARS-CoV-2 During Pregnancy.

Authors:  Andrea G Edlow; Victor M Castro; Lydia L Shook; Anjali J Kaimal; Roy H Perlis
Journal:  JAMA Netw Open       Date:  2022-06-01

2.  Bias Analysis Gone Bad.

Authors:  Timothy L Lash; Thomas P Ahern; Lindsay J Collin; Matthew P Fox; Richard F MacLehose
Journal:  Am J Epidemiol       Date:  2021-08-01       Impact factor: 4.897

3.  Are E-values too optimistic or too pessimistic? Both and neither!

Authors:  Arvid Sjölander; Sander Greenland
Journal:  Int J Epidemiol       Date:  2022-05-09       Impact factor: 9.685

4.  Are Greenland, Ioannidis and Poole opposed to the Cornfield conditions? A defence of the E-value.

Authors:  Tyler J VanderWeele
Journal:  Int J Epidemiol       Date:  2022-05-09       Impact factor: 9.685

5.  E-values for effect heterogeneity and approximations for causal interaction.

Authors:  Maya B Mathur; Louisa H Smith; Kazuki Yoshida; Peng Ding; Tyler J VanderWeele
Journal:  Int J Epidemiol       Date:  2022-08-10       Impact factor: 9.685

6.  The Importance of Making Assumptions in Bias Analysis.

Authors:  Richard F MacLehose; Thomas P Ahern; Timothy L Lash; Charles Poole; Sander Greenland
Journal:  Epidemiology       Date:  2021-09-01       Impact factor: 4.860
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.