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Unmeasured confounding and hazard scales: sensitivity analysis for total, direct, and indirect effects

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References

  1. Nordahl H, Rod NH, Frederiksen BL, Andersen I, Lange T, Diderichsen F, Prescott E, Overvad K, Osler M. Education and risk of coronary heart disease: Assessment of mediation by behavioral risk factors using the additive hazards model. Eur J Epidemiol. 2012. doi:10.1007/s10654-012-9745-z

  2. Lange T, Hansen JV. Direct and indirect effects in a survival context. Epidemiology. 2012;2011(22):575–81.

    Google Scholar 

  3. VanderWeele TJ. Causal mediation analysis with survival data. Epidemiology. 2011;22:575–81.

    Article  Google Scholar 

  4. Martinussen T, Vansteelandt S, Gerster M, von Bornemann Hjelmborg J. Estimation of direct effects for survival data by using the Aalen additive hazards model. J R Stat Soc Ser B. 2011;73:773–88.

    Article  Google Scholar 

  5. Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology. 1992;3:143–55.

    Article  PubMed  CAS  Google Scholar 

  6. Pearl J (2001). Direct and Indirect Effects. In: Proceedings of the seventeenth conference on uncertainty in artificial intelligence, Morgan Kaufmann, San Francisco, pp 411–420.

  7. VanderWeele TJ, Vansteelandt S. Odds ratios for mediation analysis with a dichotomous outcome. Am J Epidemiol. 2010;172:1339–48.

    Article  PubMed  Google Scholar 

  8. VanderWeele TJ. Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology. 2010;21:540–51.

    Article  PubMed  Google Scholar 

  9. Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychol Methods. 2010;15(4):309–34.

    Article  PubMed  Google Scholar 

  10. Valeri L, VanderWeele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods (in press).

  11. Cornfield J, Haenszel W, Hammond EC, Lilienfeld AM, Shimkin MB, Wynder LL. Smoking and lung cancer: recent evidence and a discussion of some questions. J Natl Cancer Instit. 1959;22:173–203.

    CAS  Google Scholar 

  12. Schlesselman JJ. Assessing effects of confounding variables. Am J Epidemiol. 1978;108:3–8.

    PubMed  CAS  Google Scholar 

  13. Rosenbaum PR, Rubin DB. Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J R Stat Soc Ser B. 1983;45:212–8.

    Google Scholar 

  14. Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. 3rd ed. Philadelphia: Lippincott; 2008.

    Google Scholar 

  15. VanderWeele TJ, Arah OA. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments and confounders. Epidemiology. 2011;22:42–52.

    Article  PubMed  Google Scholar 

  16. Lin DY, Psaty BM, Kronmal RA. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics. 1998;54:948–63.

    Article  PubMed  CAS  Google Scholar 

  17. Hernán MA, Robins JM. Letter to the editor of biometrics. Biometrics. 1999;55:1316–7.

    PubMed  Google Scholar 

  18. VanderWeele TJ. Sensitivity analysis: distributional assumptions and confounding assumptions. Biometrics. 2008;64:645–9.

    Article  PubMed  Google Scholar 

  19. Tchetgen Tchetgen EJ. On causal mediation analysis with a survival outcome. Int J Biostat. 2011;7(Article 33):1–38.

    Article  Google Scholar 

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Acknowledgments

This research was supported by NIH grant R01 ES017876.

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Authors and Affiliations

  1. Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Tyler J. VanderWeele

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  1. Tyler J. VanderWeele
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Correspondence to Tyler J. VanderWeele.

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VanderWeele, T.J. Unmeasured confounding and hazard scales: sensitivity analysis for total, direct, and indirect effects. Eur J Epidemiol 28, 113–117 (2013). https://doi.org/10.1007/s10654-013-9770-6

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