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Items: 1 to 20 of 93

1.

Using Bounds to Compare the Strength of Exchangeability Assumptions for Internal and External Validity.

Breskin A, Westreich D, Cole SR, Edwards JK.

Am J Epidemiol. 2019 Mar 5. pii: kwz060. doi: 10.1093/aje/kwz060. [Epub ahead of print]

PMID:
30834430
2.

Analytic Bounds on Causal Risk Differences in Directed Acyclic Graphs Involving Three Observed Binary Variables.

Kaufman S, Kaufman JS, Maclehose RF.

J Stat Plan Inference. 2009 Oct 1;139(10):3473-3487.

3.

Nonparametric Bounds and Sensitivity Analysis of Treatment Effects.

Richardson A, Hudgens MG, Gilbert PB, Fine JP.

Stat Sci. 2014 Nov;29(4):596-618.

4.

Using secondary outcome to sharpen bounds for treatment harm rate in characterizing heterogeneity.

Yin Y, Cai Z, Zhou XH.

Biom J. 2018 Sep;60(5):879-892. doi: 10.1002/bimj.201700049. Epub 2018 Jun 17.

PMID:
29911355
5.

An introduction to instrumental variable assumptions, validation and estimation.

Lousdal ML.

Emerg Themes Epidemiol. 2018 Jan 22;15:1. doi: 10.1186/s12982-018-0069-7. eCollection 2018.

6.
7.

Sharpening bounds on principal effects with covariates.

Long DM, Hudgens MG.

Biometrics. 2013 Dec;69(4):812-9. doi: 10.1111/biom.12103. Epub 2013 Nov 18.

8.

Alternative monotonicity assumptions for improving bounds on natural direct effects.

Chiba Y, Taguri M.

Int J Biostat. 2013 Jul 26;9(2):235-49. doi: 10.1515/ijb-2012-0022.

PMID:
23893690
9.

You Can't Drive a Car with Only Three Wheels.

Banack HR.

Am J Epidemiol. 2019 May 20. pii: kwz119. doi: 10.1093/aje/kwz119. [Epub ahead of print]

PMID:
31107525
10.

Bounding the per-protocol effect in randomized trials: an application to colorectal cancer screening.

Swanson SA, Holme Ø, Løberg M, Kalager M, Bretthauer M, Hoff G, Aas E, Hernán MA.

Trials. 2015 Nov 30;16:541. doi: 10.1186/s13063-015-1056-8.

11.

Formulating and Answering High-Impact Causal Questions in Physiologic Childbirth Science: Concepts and Assumptions.

Snowden JM, Tilden EL, Odden MC.

J Midwifery Womens Health. 2018 Nov;63(6):721-730. doi: 10.1111/jmwh.12868. Epub 2018 Jun 8. Review.

PMID:
29883521
12.

Antiretroviral Therapy and Mortality in Rural South Africa: A Comparison of Causal Modeling Approaches.

Oldenburg CE, Seage GR, Tanser F, De Gruttola V, Mayer KH, Mimiaga MJ, Bor J, Bärnighausen T.

Am J Epidemiol. 2018 Aug 1;187(8):1772-1779. doi: 10.1093/aje/kwy065.

13.

Generalizing Study Results: A Potential Outcomes Perspective.

Lesko CR, Buchanan AL, Westreich D, Edwards JK, Hudgens MG, Cole SR.

Epidemiology. 2017 Jul;28(4):553-561. doi: 10.1097/EDE.0000000000000664. Review. Erratum in: Epidemiology. 2018 Mar;29(2):e16.

14.
15.

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
16.

Monotone Confounding, Monotone Treatment Selection and Monotone Treatment Response.

VanderWeele TJ, Jiang Z, Chiba Y.

J Causal Inference. 2014 Mar;2(1):1-12.

17.

Bounds on controlled direct effects under monotonic assumptions about mediators and confounders.

Chiba Y.

Biom J. 2010 Oct;52(5):628-37. doi: 10.1002/bimj.201000051.

PMID:
20886528
18.

Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials.

Anglemyer A, Horvath HT, Bero L.

Cochrane Database Syst Rev. 2014 Apr 29;(4):MR000034. doi: 10.1002/14651858.MR000034.pub2. Review.

PMID:
24782322
19.

Estimating bounds on causal effects in high-dimensional and possibly confounded systems.

Malinsky D, Spirtes P.

Int J Approx Reason. 2017 Sep;88:371-384. doi: 10.1016/j.ijar.2017.06.005. Epub 2017 Jun 23.

20.

Using survival information in truncation by death problems without the monotonicity assumption.

Yang F, Ding P.

Biometrics. 2018 Dec;74(4):1232-1239. doi: 10.1111/biom.12883. Epub 2018 Apr 17.

PMID:
29665626

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