140 related articles for article (PubMed ID: 29992547)
1. Model assisted sensitivity analyses for hidden bias with binary outcomes.
Nattino G; Lu B
Biometrics; 2018 Dec; 74(4):1141-1149. PubMed ID: 29992547
[TBL] [Abstract][Full Text] [Related]
2. Sensitivity analysis and power for instrumental variable studies.
Wang X; Jiang Y; Zhang NR; Small DS
Biometrics; 2018 Dec; 74(4):1150-1160. PubMed ID: 29603714
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Assessing the impact of unmeasured confounding for binary outcomes using confounding functions.
Kasza J; Wolfe R; Schuster T
Int J Epidemiol; 2017 Aug; 46(4):1303-1311. PubMed ID: 28338913
[TBL] [Abstract][Full Text] [Related]
5. The sign of the unmeasured confounding bias under various standard populations.
Chiba Y
Biom J; 2009 Aug; 51(4):670-6. PubMed ID: 19650054
[TBL] [Abstract][Full Text] [Related]
6. Calibrating sensitivity analyses to observed covariates in observational studies.
Hsu JY; Small DS
Biometrics; 2013 Dec; 69(4):803-11. PubMed ID: 24328711
[TBL] [Abstract][Full Text] [Related]
7. Testing causal effects in observational survival data using propensity score matching design.
Lu B; Cai D; Tong X
Stat Med; 2018 May; 37(11):1846-1858. PubMed ID: 29399833
[TBL] [Abstract][Full Text] [Related]
8. A powerful approach to the study of moderate effect modification in observational studies.
Lee K; Small DS; Rosenbaum PR
Biometrics; 2018 Dec; 74(4):1161-1170. PubMed ID: 29738603
[TBL] [Abstract][Full Text] [Related]
9. Unmeasured Confounding in Observational Studies with Multiple Treatment Arms: Comparing Emergency Department Mortality of Severe Trauma Patients by Trauma Center Level.
Shi J; Lu B; Wheeler KK; Xiang H
Epidemiology; 2016 Sep; 27(5):624-32. PubMed ID: 27276025
[TBL] [Abstract][Full Text] [Related]
10. On a preference-based instrumental variable approach in reducing unmeasured confounding-by-indication.
Li Y; Lee Y; Wolfe RA; Morgenstern H; Zhang J; Port FK; Robinson BM
Stat Med; 2015 Mar; 34(7):1150-68. PubMed ID: 25546152
[TBL] [Abstract][Full Text] [Related]
11. G-computation demonstration in causal mediation analysis.
Wang A; Arah OA
Eur J Epidemiol; 2015 Oct; 30(10):1119-27. PubMed ID: 26537707
[TBL] [Abstract][Full Text] [Related]
12. Bias Analysis for Uncontrolled Confounding in the Health Sciences.
Arah OA
Annu Rev Public Health; 2017 Mar; 38():23-38. PubMed ID: 28125388
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses.
Palmer TM; Thompson JR; Tobin MD; Sheehan NA; Burton PR
Int J Epidemiol; 2008 Oct; 37(5):1161-8. PubMed ID: 18463132
[TBL] [Abstract][Full Text] [Related]
15. The missing cause approach to unmeasured confounding in pharmacoepidemiology.
Abrahamowicz M; Bjerre LM; Beauchamp ME; LeLorier J; Burne R
Stat Med; 2016 Mar; 35(7):1001-16. PubMed ID: 26932124
[TBL] [Abstract][Full Text] [Related]
16. Hierarchical priors for bias parameters in Bayesian sensitivity analysis for unmeasured confounding.
McCandless LC; Gustafson P; Levy AR; Richardson S
Stat Med; 2012 Feb; 31(4):383-96. PubMed ID: 22253142
[TBL] [Abstract][Full Text] [Related]
17. Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases.
Wendling T; Jung K; Callahan A; Schuler A; Shah NH; Gallego B
Stat Med; 2018 Oct; 37(23):3309-3324. PubMed ID: 29862536
[TBL] [Abstract][Full Text] [Related]
18. A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding.
McCandless LC; Gustafson P
Stat Med; 2017 Aug; 36(18):2887-2901. PubMed ID: 28386994
[TBL] [Abstract][Full Text] [Related]
19. Sharp nonparametric bounds and randomization inference for treatment effects on an ordinal outcome.
Chiba Y
Stat Med; 2017 Nov; 36(25):3966-3975. PubMed ID: 28703430
[TBL] [Abstract][Full Text] [Related]
20. How to control for unmeasured confounding in an observational time-to-event study with exposure incidence information: the treatment choice Cox model.
Troendle J; Leifer E; Zhang Z; Yang S; Tewes H
Stat Med; 2017 Oct; 36(23):3654-3669. PubMed ID: 28675922
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
