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BioMed Research International
Volume 2014, Article ID 872435, 8 pages
http://dx.doi.org/10.1155/2014/872435
Research Article

A Proxy Outcome Approach for Causal Effect in Observational Studies: A Simulation Study

1National Drug Research Institute, Health Science, Curtin University, G.P.O. Box U 1987, Perth, WA 6845, Australia
2Northern Territory Department of Health, Darwin, NT 0800, Australia
3School of Public Health, Health Science, Curtin University, Perth, WA 6845, Australia

Received 1 November 2013; Revised 3 January 2014; Accepted 3 January 2014; Published 18 February 2014

Academic Editor: Abdelwahab Omri

Copyright © 2014 Wenbin Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Y. Lin, B. M. Psaty, and R. A. Kronmal, “Assessing the sensitivity of regression results to unmeasured confounders in observational studies,” Biometrics, vol. 54, no. 3, pp. 948–963, 1998. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Greenland, “The impact of prior distributions for uncontrolled confounding and response bias: a case study of the relation of wire codes and magnetic fields to childhood leukemia,” Journal of the American Statistical Association, vol. 98, no. 461, pp. 47–54, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Greenland, J. Copas, D. R. Jones et al., “Multiple-bias modelling for analysis of observational data,” Journal of the Royal Statistical Society A, vol. 168, no. 2, pp. 267–306, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. L. C. McCandless, P. Gustafson, and A. Levy, “Bayesian sensitivity analysis for unmeasured confounding in observational studies,” Statistics in Medicine, vol. 26, no. 11, pp. 2331–2347, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. O. A. Arah, Y. Chiba, and S. Greenland, “Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders,” Annals of Epidemiology, vol. 18, no. 8, pp. 637–646, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. T. M. Palmer, J. R. Thompson, M. D. Tobin, N. A. Sheehan, and P. R. Burton, “Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses,” International Journal of Epidemiology, vol. 37, no. 5, pp. 1161–1168, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. T. J. VanderWeele, M. A. Hernán, and J. M. Robins, “Causal directed acyclic graphs and the direction of unmeasured confounding bias,” Epidemiology, vol. 19, no. 5, pp. 720–728, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Chiba, “The sign of the unmeasured confounding bias under various standard populations,” Biometrical Journal, vol. 51, no. 4, pp. 670–676, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. W.-C. Lee, “Bounding the bias of unmeasured factors with confounding and effect-modifying potentials,” Statistics in Medicine, vol. 30, no. 9, pp. 1007–1017, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. T. J. Vanderweele and O. A. Arah, “Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders,” Epidemiology, vol. 22, no. 1, pp. 42–52, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. L. C. McCandless, P. Gustafson, A. R. Levy, and S. Richardson, “Hierarchical priors for bias parameters in Bayesian sensitivity analysis for unmeasured confounding,” Statistics in Medicine, vol. 31, no. 4, pp. 383–396, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. F. Jacobi, H.-U. Wittchen, C. Hölting et al., “Prevalence, co-morbidity and correlates of mental disorders in the general population: results from the German Health Interview and Examination Survey (GHS),” Psychological Medicine, vol. 34, no. 4, pp. 597–611, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. Y.-F. Chan, M. L. Dennis, and R. R. Funk, “Prevalence and comorbidity of major internalizing and externalizing problems among adolescents and adults presenting to substance abuse treatment,” Journal of Substance Abuse Treatment, vol. 34, no. 1, pp. 14–24, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Teesson, W. Hall, T. Slade et al., “Prevalence and correlates of DSM-IV alcohol abuse and dependence in Australia: findings of the 2007 National Survey of Mental Health and Wellbeing,” Addiction, vol. 105, no. 12, pp. 2085–2094, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Liang and T. Chikritzhs, “Does light alcohol consumption during pregnancy improve offspring's cognitive development?” Medical Hypotheses, vol. 78, no. 1, pp. 69–70, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Liang and T. Chikritzhs, “Alcohol consumption and health status of family members: health impacts without ingestion,” Internal Medicine Journal, vol. 43, no. 9, pp. 1012–1016, 2013. View at Publisher · View at Google Scholar
  17. E. Petridou, X. Zavitsanos, N. Dessypris et al., “Adolescents in high-risk trajectory: clustering of risky behavior and the origins of socioeconomic health differentials,” Preventive Medicine, vol. 26, no. 2, pp. 215–219, 1997. View at Publisher · View at Google Scholar · View at Scopus
  18. J. W. Lynch, G. A. Kaplan, and J. T. Salonen, “Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse,” Social Science and Medicine, vol. 44, no. 6, pp. 809–819, 1997. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Rozanski, J. A. Blumenthal, K. W. Davidson, P. G. Saab, and L. Kubzansky, “The epidemiology, pathophysiology, and management of psychosocial risk factors in cardiac practice: the emerging field of behavioral cardiology,” Journal of the American College of Cardiology, vol. 45, no. 5, pp. 637–651, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. K.-J. Suzuki, S. Nakaji, S. Tokunaga, T. Shimoyama, T. Umeda, and K. Sugawara, “Confounding by dietary factors in case-control studies on the efficacy of cancer screening in Japan,” European Journal of Epidemiology, vol. 20, no. 1, pp. 73–78, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. R. H. H. Groenwold, A. W. Hoes, and E. Hak, “Confounding in publications of observational intervention studies,” European Journal of Epidemiology, vol. 22, no. 7, pp. 413–415, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. P. Lagiou, H.-O. Adami, and D. Trichopoulos, “Causality in cancer epidemiology,” European Journal of Epidemiology, vol. 20, no. 7, pp. 565–574, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. K. Herttua, P. Mäkelä, and P. Martikainen, “An evaluation of the impact of a large reduction in alcohol prices on alcohol-related and all-cause mortality: time series analysis of a population-based natural experiment,” International Journal of Epidemiology, vol. 40, no. 2, Article ID dyp336, pp. 441–454, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. E. Poirier, L. Watier, E. Espie, F.-X. Weill, H. de Valk, and J.-C. Desenclos, “Evaluation of the impact on human salmonellosis of control measures targeted to Salmonella Enteritidis and Typhimurium in poultry breeding using time-series analysis and intervention models in France,” Epidemiology and Infection, vol. 136, no. 9, pp. 1217–1224, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. K. J. Rothman, S. Greenland, C. Poole, and T. L. Lash, “Causation and causal inference,” Modern Epidemiology, vol. 2, pp. 7–28, 1998. View at Google Scholar
  26. W. Liang and T. Chikritzhs, “Observational research on alcohol use and chronic disease outcome: new approaches to counter biases,” The Scientific World Journal, vol. 2013, Article ID 860915, 14 pages, 2013. View at Publisher · View at Google Scholar
  27. M. Zuckerman and D. M. Kuhlman, “Personality and risk-taking: common biosocial factors,” Journal of Personality, vol. 68, no. 6, pp. 999–1029, 2000. View at Google Scholar · View at Scopus
  28. R. C. Kessler, C. B. Nelson, K. A. McGonagle, M. J. Edlund, R. G. Frank, and P. J. Leaf, “The epidemiology of co-occurring addictive and mental disorders: implications for prevention and service utilization,” American Journal of Orthopsychiatry, vol. 66, no. 1, pp. 17–31, 1996. View at Publisher · View at Google Scholar · View at Scopus