189 related articles for article (PubMed ID: 20449636)
1. The Janus face of statistical adjustment: confounders versus colliders.
Janszky I; Ahlbom A; Svensson AC
Eur J Epidemiol; 2010 Jun; 25(6):361-3. PubMed ID: 20449636
[TBL] [Abstract][Full Text] [Related]
2. [Application of directed acyclic graphs in identifying and controlling confounding bias].
Liu HX; Wang HB; Wang N
Zhonghua Liu Xing Bing Xue Za Zhi; 2020 Apr; 41(4):585-588. PubMed ID: 32344486
[TBL] [Abstract][Full Text] [Related]
3. On the nondifferential misclassification of a binary confounder.
Ogburn EL; VanderWeele TJ
Epidemiology; 2012 May; 23(3):433-9. PubMed ID: 22450692
[TBL] [Abstract][Full Text] [Related]
4. Causal Methods for Observational Research: A Primer.
Almasi-Hashiani A; Nedjat S; Mansournia MA
Arch Iran Med; 2018 Apr; 21(4):164-169. PubMed ID: 29693407
[TBL] [Abstract][Full Text] [Related]
5. Can statistical adjustment guided by causal inference improve the accuracy of effect estimation? A simulation and empirical research based on meta-analyses of case-control studies.
Yan R; Liu T; Peng Y; Peng X
BMC Med Inform Decis Mak; 2020 Dec; 20(1):333. PubMed ID: 33308213
[TBL] [Abstract][Full Text] [Related]
6. "Toward a clearer definition of confounding" revisited with directed acyclic graphs.
Howards PP; Schisterman EF; Poole C; Kaufman JS; Weinberg CR
Am J Epidemiol; 2012 Sep; 176(6):506-11. PubMed ID: 22904203
[TBL] [Abstract][Full Text] [Related]
7. Analytic results on the bias due to nondifferential misclassification of a binary mediator.
Ogburn EL; VanderWeele TJ
Am J Epidemiol; 2012 Sep; 176(6):555-61. PubMed ID: 22930481
[TBL] [Abstract][Full Text] [Related]
8. Reducing bias in pelvic floor disorders research: using directed acyclic graphs as an aid.
Sung VW
Neurourol Urodyn; 2012 Jan; 31(1):115-20. PubMed ID: 21826724
[TBL] [Abstract][Full Text] [Related]
9. Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models.
Wang C; Dominici F; Parmigiani G; Zigler CM
Biometrics; 2015 Sep; 71(3):654-65. PubMed ID: 25899155
[TBL] [Abstract][Full Text] [Related]
10. Limits for the Magnitude of M-bias and Certain Other Types of Structural Selection Bias.
Flanders WD; Ye D
Epidemiology; 2019 Jul; 30(4):501-508. PubMed ID: 31033689
[TBL] [Abstract][Full Text] [Related]
11. Directed Acyclic Graphs for Oral Disease Research.
Akinkugbe AA; Sharma S; Ohrbach R; Slade GD; Poole C
J Dent Res; 2016 Jul; 95(8):853-9. PubMed ID: 27000052
[TBL] [Abstract][Full Text] [Related]
12. Effect decomposition in the presence of an exposure-induced mediator-outcome confounder.
Vanderweele TJ; Vansteelandt S; Robins JM
Epidemiology; 2014 Mar; 25(2):300-6. PubMed ID: 24487213
[TBL] [Abstract][Full Text] [Related]
13. [Confounder adjustment in observational comparative effectiveness researches: (2) statistical adjustment approaches for unmeasured confounders].
Huang LL; Wei YY; Chen F
Zhonghua Liu Xing Bing Xue Za Zhi; 2019 Nov; 40(11):1450-1455. PubMed ID: 31838820
[TBL] [Abstract][Full Text] [Related]
14. Causal Diagrams: Pitfalls and Tips.
Suzuki E; Shinozaki T; Yamamoto E
J Epidemiol; 2020 Apr; 30(4):153-162. PubMed ID: 32009103
[TBL] [Abstract][Full Text] [Related]
15. The control outcome calibration approach for causal inference with unobserved confounding.
Tchetgen Tchetgen E
Am J Epidemiol; 2014 Mar; 179(5):633-40. PubMed ID: 24363326
[TBL] [Abstract][Full Text] [Related]
16. Methods of Public Health Research - Strengthening Causal Inference from Observational Data.
Hernán MA
N Engl J Med; 2021 Oct; 385(15):1345-1348. PubMed ID: 34596980
[No Abstract] [Full Text] [Related]
17. How to implement directed acyclic graphs to reduce bias in addiction research.
Wouk K; Bauer AE; Gottfredson NC
Addict Behav; 2019 Jul; 94():109-116. PubMed ID: 30292573
[No Abstract] [Full Text] [Related]
18. Adjusting for indirectly measured confounding using large-scale propensity score.
Zhang L; Wang Y; Schuemie MJ; Blei DM; Hripcsak G
J Biomed Inform; 2022 Oct; 134():104204. PubMed ID: 36108816
[TBL] [Abstract][Full Text] [Related]
19. Bounding Bias Due to Selection.
Smith LH; VanderWeele TJ
Epidemiology; 2019 Jul; 30(4):509-516. PubMed ID: 31033690
[TBL] [Abstract][Full Text] [Related]
20. Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.
Schuler MS; Rose S
Am J Epidemiol; 2017 Jan; 185(1):65-73. PubMed ID: 27941068
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
