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Sensitivity analysis for interactions under unmeasured confounding
dc.contributor.authorSchisterman, Enrique F.en_US
dc.contributor.authorAlbert, Paul S.en_US
dc.date.accessioned2012-10-02T17:20:02Z
dc.date.available2013-10-18T17:47:30Zen_US
dc.date.issued2012-09-28en_US
dc.identifier.citationSchisterman, Enrique F.; Albert, Paul S. (2012). "Sensitivity analysis for interactions under unmeasured confounding." Statistics in Medicine 31(22): 2552-2564. <http://hdl.handle.net/2027.42/93672>en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/93672
dc.publisherSpringer‐Verlagen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherBias Analysisen_US
dc.subject.otherSensitivity Analysisen_US
dc.subject.otherInteractionen_US
dc.subject.otherIndependenceen_US
dc.subject.otherGene Environmenten_US
dc.subject.otherUnmeasured Confoundingen_US
dc.titleSensitivity analysis for interactions under unmeasured confoundingen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/93672/1/sim4354.pdf
dc.identifier.doi10.1002/sim.4354en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.identifier.citedreferenceVanderWeele TJ. Sufficient cause interactions and statistical interactions. Epidemiology 2009; 20: 6 – 13.en_US
dc.identifier.citedreferenceNeyman J. Sur les applications de la thar des probabilities aux experiences Agaricales: essay des principle. (1923). Excerpts reprinted in English (D. Dabrowska and T. Speed, Trans.) Statistical Science 1990; 5: 463 – 472.en_US
dc.identifier.citedreferenceBlot WJ, Day NE. Synergism and interaction: are they equivalent? American Journal of Epidemiology 1979; 110: 99 – 100.en_US
dc.identifier.citedreferenceRothman KJ, Greenland S, Walker AM. Concepts of interaction. American Journal of Epidemiology 1980; 112: 467 – 470.en_US
dc.identifier.citedreferenceSaracci R. Interaction and synergism. American Journal of Epidemiology 1980; 112: 465 – 466.en_US
dc.identifier.citedreferenceRothman KJ, Greenland S, Lash TL. Modern Epidemiology, 3rd edn. Lippincott Williams & Wilkins: Philadelphia, 2008.en_US
dc.identifier.citedreferenceVanderWeele TJ, Robins JM. The identification of synergism in the suffcient‐component cause framework. Epidemiology 2007; 18: 329 – 339.en_US
dc.identifier.citedreferenceVanderWeele TJ. Empirical tests for compositional epistasis. Nature Reviews Genetics 2010; 11: 166.en_US
dc.identifier.citedreferenceGreenland S. Tests for interaction in epidemiologic studies: a review and a study of power. Statistics in Medicine 1983; 2: 243 – 251.en_US
dc.identifier.citedreferenceAhsan H, Chen Y, Parvez F, Zablotska L, Argos M, Hussain I, Momotaj H, Levy D, Cheng Z, Slavkovich V, van Geen A, Howe GR, Graziano JH. Arsenic exposure from drinking water and risk of premalignant skin lesions in Bangladesh: baseline results from the Health Effects of Arsenic Longitudinal Study. American Journal of Epidemiology 2006; 163: 1138 – 1148.en_US
dc.identifier.citedreferenceBennett WP, Alavanja MCR, Blomeke B, Vähäkangas KH, Castrén K, Welsh JA, Bowman ED, Khan MA, Flieder DB, Harris CC. Environmental tobacco smoke, genetic susceptibility, and risk of lung cancer in never‐smoking women. Journal of the National Cancer Institute 1999; 91: 2009 – 2014.en_US
dc.identifier.citedreferencePiegorsch WW, Weinberg CR, Taylor JA. Non‐hierarchical logistic models and case‐only designs for assessing susceptibility in population‐based case‐control studies. Statistics in Medicine 1994; 13: 153 – 162.en_US
dc.identifier.citedreferenceKhoury MJ, Flanders WD. Nontraditional epidemiologic approaches in the analysis of gene‐environment interaction: case‐control studies with no controls! American Journal of Epidemiology 1996; 144: 207 – 213.en_US
dc.identifier.citedreferenceVanderWeele TJ, Knol MJ. The interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions. Annals of Internal Medicine 2011; 154: 680 – 683.en_US
dc.identifier.citedreferenceVanderWeele TJ. On the distinction between interaction and effect modification. Epidemiology 2009; 20: 863 – 871.en_US
dc.identifier.citedreferenceAssmann SF, Hosmer DW, Lemeshow S, Mundt KA. Confidence intervals for measures of interaction. Epidemiology 1996; 7: 286 – 290.en_US
dc.identifier.citedreferenceHosmer DW, Lemeshow S. Confidence interval estimation of interaction. Epidemiology 1992; 3: 452 – 456.en_US
dc.identifier.citedreferenceRothman KJ. Modern Epidemiology, 1st edn. Little, Brown and Company: Boston, MA, 1986.en_US
dc.identifier.citedreferenceCornfield J, Haenszel W, Hammond EC, Lilienfeld AM, Shimkin MB, Wynder LL. Smoking and lung cancer: recent evidence and a discussion of some questions. Journal of the National Cancer Institute 1959; 22: 173 – 203.en_US
dc.identifier.citedreferenceRosenbaum PR, Rubin DB. Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. Journal of the Royal Statistical Society Series B 1983; 45: 212 – 218.en_US
dc.identifier.citedreferenceFlanders WD, Khoury MJ. Indirect assessment of confounding: graphic description and limits on effect of adjusting for covariates. Epidemiology 1990; 1: 239 – 246.en_US
dc.identifier.citedreferenceLin DY, Psaty BM, Kronmal RA. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 1998; 54: 948 – 963.en_US
dc.identifier.citedreferenceRobins JM, Scharfstein D, Rotnitzky A. Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In Statistical Models for Epidemiology, the Environment, and Clinical Trials, Halloran E, Berry D (eds). Springer‐Verlag: New York, 2000; 1 – 95.en_US
dc.identifier.citedreferenceRosenbaum PR. Observational Studies, 2nd edn. Springer‐Verlag: New York, 2002.en_US
dc.identifier.citedreferenceVanderWeele TJ, Arah OA. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments and confounders. Epidemiology 2011; 22: 42 – 52.en_US
dc.identifier.citedreferenceRubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 1974; 66: 688 – 701.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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