148 related articles for article (PubMed ID: 32995847)
1. Bias factor, maximum bias and the E-value: insight and extended applications.
Cusson A; Infante-Rivard C
Int J Epidemiol; 2020 Oct; 49(5):1509-1516. PubMed ID: 32995847
[TBL] [Abstract][Full Text] [Related]
2. Assessing the impact of unmeasured confounders for credible and reliable real-world evidence.
Zhang X; Stamey JD; Mathur MB
Pharmacoepidemiol Drug Saf; 2020 Oct; 29(10):1219-1227. PubMed ID: 32929830
[TBL] [Abstract][Full Text] [Related]
3. Adjustment for unmeasured confounding through informative priors for the confounder-outcome relation.
Groenwold RHH; Shofty I; Miočević M; van Smeden M; Klugkist I
BMC Med Res Methodol; 2018 Dec; 18(1):174. PubMed ID: 30577773
[TBL] [Abstract][Full Text] [Related]
4. Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders.
Arah OA; Chiba Y; Greenland S
Ann Epidemiol; 2008 Aug; 18(8):637-46. PubMed ID: 18652982
[TBL] [Abstract][Full Text] [Related]
5. Sensitivity Analysis Without Assumptions.
Ding P; VanderWeele TJ
Epidemiology; 2016 May; 27(3):368-77. PubMed ID: 26841057
[TBL] [Abstract][Full Text] [Related]
6. Assessing Techniques for Quantifying the Impact of Bias Due to an Unmeasured Confounder: An Applied Example.
Barberio J; Ahern TP; MacLehose RF; Collin LJ; Cronin-Fenton DP; Damkier P; Sørensen HT; Lash TL
Clin Epidemiol; 2021; 13():627-635. PubMed ID: 34349564
[TBL] [Abstract][Full Text] [Related]
7. A comparison of methods to estimate the survivor average causal effect in the presence of missing data: a simulation study.
McGuinness MB; Kasza J; Karahalios A; Guymer RH; Finger RP; Simpson JA
BMC Med Res Methodol; 2019 Dec; 19(1):223. PubMed ID: 31795945
[TBL] [Abstract][Full Text] [Related]
8. How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies.
Barrowman MA; Peek N; Lambie M; Martin GP; Sperrin M
BMC Med Res Methodol; 2019 Jul; 19(1):166. PubMed ID: 31366331
[TBL] [Abstract][Full Text] [Related]
9. Understanding the impact of non-shared unmeasured confounding on the sibling comparison analysis.
Esen BÖ; Ehrenstein V; Petersen I; Sørensen HT; Pedersen L
Int J Epidemiol; 2024 Feb; 53(1):. PubMed ID: 38110565
[TBL] [Abstract][Full Text] [Related]
10. [Sensitivity analysis method for unmeasured confounding interference in observational study].
Wang DH; You DF; Huang LL; Zhao Y
Zhonghua Liu Xing Bing Xue Za Zhi; 2019 Nov; 40(11):1470-1475. PubMed ID: 31838823
[No Abstract] [Full Text] [Related]
11. An E-value analysis of potential unmeasured or residual confounding in systematic reviews of post-tuberculosis mortality, respiratory disease, and cardiovascular disease.
Basham CA; Karim ME
Ann Epidemiol; 2022 Apr; 68():24-31. PubMed ID: 34973421
[TBL] [Abstract][Full Text] [Related]
12. The impact of unmeasured within- and between-cluster confounding on the bias of effect estimatorsof a continuous exposure.
Li Y; Lee Y; Port FK; Robinson BM
Stat Methods Med Res; 2020 Aug; 29(8):2119-2139. PubMed ID: 31694489
[TBL] [Abstract][Full Text] [Related]
13. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders.
Vanderweele TJ; Arah OA
Epidemiology; 2011 Jan; 22(1):42-52. PubMed ID: 21052008
[TBL] [Abstract][Full Text] [Related]
14. Two-stage instrumental variable methods for estimating the causal odds ratio: analysis of bias.
Cai B; Small DS; Have TR
Stat Med; 2011 Jul; 30(15):1809-24. PubMed ID: 21495062
[TBL] [Abstract][Full Text] [Related]
15. Application of quantitative bias analysis for unmeasured confounding in cost-effectiveness modelling.
Leahy TP; Duffield S; Kent S; Sammon C; Tzelis D; Ray J; Groenwold RH; Gomes M; Ramagopalan S; Grieve R
J Comp Eff Res; 2022 Aug; 11(12):861-870. PubMed ID: 35678168
[TBL] [Abstract][Full Text] [Related]
16. Implications of M bias in epidemiologic studies: a simulation study.
Liu W; Brookhart MA; Schneeweiss S; Mi X; Setoguchi S
Am J Epidemiol; 2012 Nov; 176(10):938-48. PubMed ID: 23100247
[TBL] [Abstract][Full Text] [Related]
17. Quantitative bias analysis in practice: review of software for regression with unmeasured confounding.
Kawabata E; Tilling K; Groenwold RHH; Hughes RA
BMC Med Res Methodol; 2023 May; 23(1):111. PubMed ID: 37142961
[TBL] [Abstract][Full Text] [Related]
18. Sensitivity analysis for the effects of multiple unmeasured confounders.
Groenwold RH; Sterne JA; Lawlor DA; Moons KG; Hoes AW; Tilling K
Ann Epidemiol; 2016 Sep; 26(9):605-11. PubMed ID: 27576907
[TBL] [Abstract][Full Text] [Related]
19. Sensitivity analysis for interactions under unmeasured confounding.
Vanderweele TJ; Mukherjee B; Chen J
Stat Med; 2012 Sep; 31(22):2552-64. PubMed ID: 21976358
[TBL] [Abstract][Full Text] [Related]
20. Bespoke Instruments: A new tool for addressing unmeasured confounders.
Richardson DB; Tchetgen Tchetgen EJ
Am J Epidemiol; 2022 Mar; 191(5):939-947. PubMed ID: 34907434
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
