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Results: 1 to 20 of 164

Similar articles for PubMed (Select 14569183)

1.

Marginal structural models as a tool for standardization.

Sato T, Matsuyama Y.

Epidemiology. 2003 Nov;14(6):680-6.

PMID:
14569183
2.

Marginal structural models and causal inference in epidemiology.

Robins JM, Hernán MA, Brumback B.

Epidemiology. 2000 Sep;11(5):550-60.

PMID:
10955408
3.

Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.

Hernán MA, Brumback B, Robins JM.

Epidemiology. 2000 Sep;11(5):561-70.

PMID:
10955409
4.

Structural accelerated failure time models for survival analysis in studies with time-varying treatments.

Hernán MA, Cole SR, Margolick J, Cohen M, Robins JM.

Pharmacoepidemiol Drug Saf. 2005 Jul;14(7):477-91.

PMID:
15660442
5.
6.

Differences between marginal structural models and conventional models in their exposure effect estimates: a systematic review.

Suarez D, Borràs R, Basagaña X.

Epidemiology. 2011 Jul;22(4):586-8. doi: 10.1097/EDE.0b013e31821d0507. Review.

PMID:
21540744
7.

Estimating causal effects from epidemiological data.

Hernán MA, Robins JM.

J Epidemiol Community Health. 2006 Jul;60(7):578-86.

8.

Estimating causal parameters without target populations.

Shahar E.

J Eval Clin Pract. 2007 Oct;13(5):814-6.

PMID:
17824877
9.

Bounds on potential risks and causal risk differences under assumptions about confounding parameters.

Chiba Y, Sato T, Greenland S.

Stat Med. 2007 Dec 10;26(28):5125-35.

PMID:
17525935
10.

Analysis of longitudinal marginal structural models.

Bryan J, Yu Z, Van Der Laan MJ.

Biostatistics. 2004 Jul;5(3):361-80.

11.

A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study.

Gran JM, Røysland K, Wolbers M, Didelez V, Sterne JA, Ledergerber B, Furrer H, von Wyl V, Aalen OO.

Stat Med. 2010 Nov 20;29(26):2757-68. doi: 10.1002/sim.4048.

PMID:
20803557
12.

Marginal structural models for partial exposure regimes.

Vansteelandt S, Mertens K, Suetens C, Goetghebeur E.

Biostatistics. 2009 Jan;10(1):46-59. doi: 10.1093/biostatistics/kxn012. Epub 2008 May 23.

13.
14.

Marginal structural models might overcome confounding when analyzing multiple treatment effects in observational studies.

Suarez D, Haro JM, Novick D, Ochoa S.

J Clin Epidemiol. 2008 Jun;61(6):525-30. doi: 10.1016/j.jclinepi.2007.11.007. Review.

PMID:
18471655
15.

Marginal structural models for sufficient cause interactions.

Vanderweele TJ, Vansteelandt S, Robins JM.

Am J Epidemiol. 2010 Feb 15;171(4):506-14. doi: 10.1093/aje/kwp396. Epub 2010 Jan 11.

16.

Bounding causal effects under uncontrolled confounding using counterfactuals.

MacLehose RF, Kaufman S, Kaufman JS, Poole C.

Epidemiology. 2005 Jul;16(4):548-55. Review.

PMID:
15951674
17.

Causal analysis of case-control data.

Newman SC.

Epidemiol Perspect Innov. 2006 Jan 27;3:2.

18.

Invited commentary: G-computation--lost in translation?

Vansteelandt S, Keiding N.

Am J Epidemiol. 2011 Apr 1;173(7):739-42. doi: 10.1093/aje/kwq474. Epub 2011 Mar 16.

19.

Use of tamoxifen in pT1a-pT1b, pN0 breast cancer.

Livi L, Saieva C, Paiar F, Simontacchi G, Galardi A, De Luca Cardillo C, Mangoni M, Paoletti L, Ponticelli P, Biti GP.

Eur J Surg Oncol. 2007 Apr;33(3):271-5. Epub 2006 Jul 10.

PMID:
16831531
20.

Acceptance of tamoxifen chemoprevention by physicians and women at risk.

Tchou J, Hou N, Rademaker A, Jordan VC, Morrow M.

Cancer. 2004 May 1;100(9):1800-6.

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