351 related articles for article (PubMed ID: 27748680)
1. Bias Due to Confounders for the Exposure-Competing Risk Relationship.
Lesko CR; Lau B
Epidemiology; 2017 Jan; 28(1):20-27. PubMed ID: 27748680
[TBL] [Abstract][Full Text] [Related]
2. On model selection and model misspecification in causal inference.
Vansteelandt S; Bekaert M; Claeskens G
Stat Methods Med Res; 2012 Feb; 21(1):7-30. PubMed ID: 21075803
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Modelling two cause-specific hazards of competing risks in one cumulative proportional odds model?
Ohneberg K; Schumacher M; Beyersmann J
Stat Med; 2017 Nov; 36(27):4353-4363. PubMed ID: 28833435
[TBL] [Abstract][Full Text] [Related]
5. Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.
Malec SA; Wei P; Bernstam EV; Boyce RD; Cohen T
J Biomed Inform; 2021 May; 117():103719. PubMed ID: 33716168
[TBL] [Abstract][Full Text] [Related]
6. A general approach to evaluating the bias of 2-stage instrumental variable estimators.
Wan F; Small D; Mitra N
Stat Med; 2018 May; 37(12):1997-2015. PubMed ID: 29572890
[TBL] [Abstract][Full Text] [Related]
7. Correcting for Measurement Error in Time-Varying Covariates in Marginal Structural Models.
Kyle RP; Moodie EE; Klein MB; Abrahamowicz M
Am J Epidemiol; 2016 Aug; 184(3):249-58. PubMed ID: 27416840
[TBL] [Abstract][Full Text] [Related]
8. The importance of censoring in competing risks analysis of the subdistribution hazard.
Donoghoe MW; Gebski V
BMC Med Res Methodol; 2017 Apr; 17(1):52. PubMed ID: 28376736
[TBL] [Abstract][Full Text] [Related]
9. Doubly robust estimators of causal exposure effects with missing data in the outcome, exposure or a confounder.
Williamson EJ; Forbes A; Wolfe R
Stat Med; 2012 Dec; 31(30):4382-400. PubMed ID: 23086504
[TBL] [Abstract][Full Text] [Related]
10. Prior event rate ratio adjustment: numerical studies of a statistical method to address unrecognized confounding in observational studies.
Yu M; Xie D; Wang X; Weiner MG; Tannen RL
Pharmacoepidemiol Drug Saf; 2012 May; 21 Suppl 2():60-8. PubMed ID: 22552981
[TBL] [Abstract][Full Text] [Related]
11. Confounder selection strategies targeting stable treatment effect estimators.
Loh WW; Vansteelandt S
Stat Med; 2021 Feb; 40(3):607-630. PubMed ID: 33150645
[TBL] [Abstract][Full Text] [Related]
12. Identification of confounders in the assessment of the relationship between lead exposure and child development.
Tong IS; Lu Y
Ann Epidemiol; 2001 Jan; 11(1):38-45. PubMed ID: 11164118
[TBL] [Abstract][Full Text] [Related]
13. A comparison of estimators from self-controlled case series, case-crossover design, and sequence symmetry analysis for pharmacoepidemiological studies.
Takeuchi Y; Shinozaki T; Matsuyama Y
BMC Med Res Methodol; 2018 Jan; 18(1):4. PubMed ID: 29310575
[TBL] [Abstract][Full Text] [Related]
14. [Causal Inference in Medicine Part II. Directed acyclic graphs--a useful method for confounder selection, categorization of potential biases, and hypothesis specification].
Suzuki E; Komatsu H; Yorifuji T; Yamamoto E; Doi H; Tsuda T
Nihon Eiseigaku Zasshi; 2009 Sep; 64(4):796-805. PubMed ID: 19797848
[TBL] [Abstract][Full Text] [Related]
15. Dealing with competing risks: testing covariates and calculating sample size.
Pintilie M
Stat Med; 2002 Nov; 21(22):3317-24. PubMed ID: 12407674
[TBL] [Abstract][Full Text] [Related]
16. Methodological evaluation of bias in observational coronavirus disease 2019 studies on drug effectiveness.
Martinuka O; von Cube M; Wolkewitz M
Clin Microbiol Infect; 2021 Jul; 27(7):949-957. PubMed ID: 33813117
[TBL] [Abstract][Full Text] [Related]
17. Invited commentary: variable selection versus shrinkage in the control of multiple confounders.
Greenland S
Am J Epidemiol; 2008 Mar; 167(5):523-9; discussion 530-1. PubMed ID: 18227100
[TBL] [Abstract][Full Text] [Related]
18. Collinearity and Causal Diagrams: A Lesson on the Importance of Model Specification.
Schisterman EF; Perkins NJ; Mumford SL; Ahrens KA; Mitchell EM
Epidemiology; 2017 Jan; 28(1):47-53. PubMed ID: 27676260
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. A competing risks approach to "biologic" interaction.
Andersen PK; Skrondal A
Lifetime Data Anal; 2015 Apr; 21(2):300-14. PubMed ID: 25613424
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
