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Bias in Clinical Studies of Genetic Diseases

  • Susan Stuckless
  • Patrick S. Parfrey
Protocol
Part of the Methods in Molecular Biology™ book series (MIMB, volume 473)

Abstract

Clinical epidemiological research in genetic diseases entails the assessment of phenotypes, the burden and etiology of disease, and the efficacy of preventive measures or treatments in populations. In all areas, the main focus is to describe the relationship between exposure and outcome and determine one of the following: prevalence, incidence, cause, prognosis, or effect of treatment. The accuracy of these conclusions is determined by the validity of the study. Validity is determined by addressing potential biases and possible confounders that may be responsible for the observed association. Therefore, it is important to understand the types of bias that exist and be able to assess their impact on the magnitude and direction of the observed effect. This chapter reviews the epidemiological concepts of selection bias, information bias, and confounding and discusses ways in which these sources of bias can be minimized.

Key words

Genetic diseases epidemiology selection bias information bias confounding validity 

References

  1. 1.
    1. Young, J. M., Solomon, M. J. (2003) Improving the evidence base in surgery: Sources of bias in surgical studies. ANZ J. Surg 73, 504–506.PubMedCrossRefGoogle Scholar
  2. 2.
    2. Jepsen, P., Johnsen, S. P., Gillman, M. W., Sorensen, H. T. (2004) Interpretation of observational studies. Heart 90, 956–960.PubMedCrossRefGoogle Scholar
  3. 3.
    3. Blackmore, C. C., Cummings, P. (2004) Observational studies in radiology. AJR 183, 1203–1208.PubMedGoogle Scholar
  4. 4.
    4. Barnett, M. L., Hyman, J. J. (2006) Challenges in interpreting study results: The conflict between appearance and reality. JADA 137, 32S–36S.PubMedGoogle Scholar
  5. 5.
    5. Stuckless, S., Parfrey, P. S., Woods, M. O., Cox, J., Fitzgerald, G. W., Green, J. S., Green, R. C. (2007) The phenotypic expression of three MSH2 mutations in large Newfoundland families with Lynch syndrome. Fam Cancer 6, 1–12.PubMedCrossRefGoogle Scholar
  6. 6.
    6. Dicks, E., Ravani, P., Langman, D., Davidson, W. S., Pei, Y., Parfrey, P. S. (2006) Incident renal events and risk factors in autosomal dominant polycystic kidney disease: A population and family based cohort followed for 22 years. Clin J Am Soc Nephrol 1, 710–717.PubMedCrossRefGoogle Scholar
  7. 7.
    7. Moore, S .J., Green, J. S., Fan, Y., Bhogal, A. K., Dicks, E., Fernandez, B. F., Stefanelli, M., Murphy, C., Cramer, B. C., Dean, J. C. S., Beales, P. L., Katsanis, N., Bassett, A., Davidson, W. S., Parfrey, P.S. (2005) Clinical and genetic epidemiology of Bardet-Biedl syndrome in Newfoundland: A 22-year prospective, population-based cohort study. Am J Med Gene. 132A, 352–360.CrossRefGoogle Scholar
  8. 8.
    8. Hodgkinson, K. A., Parfrey, P. S., Bassett, A. S., Kupprion, C., Drenckhahn, J., Norman, M. W., Thierfelder, L., Stuckless, S. N., Dicks, E. L., McKenna, W. J., Connors, S. P. (2005) The impact of implantable cardioverter-defibrillator therapy on survival in autosomal-dominant arrhythmogenic right ventricular cardiomyopathy (ARVD5). J Am Coll Cardiol 45, 400–408.PubMedCrossRefGoogle Scholar
  9. 9.
    9. Sica, G. T. (2006) Bias in research studies. Radiology 238, 780–789.PubMedCrossRefGoogle Scholar
  10. 10.
    10. Zaccai, J. H. (2004) How to assess epidemiological studies. Postgrad. Med. J 80, 140–147.PubMedCrossRefGoogle Scholar
  11. 11.
    11. Moles, D. R., dos Santos Silva, I. (2000) Causes, associations and evaluating evidence; can we trust what we read? Evidence-Based Dentistry 2, 75–78.CrossRefGoogle Scholar
  12. 12.
    12. Dore, D. D., Larrat, E. P., Vogenberg, F. R. (2006) Principles of epidemiology for clinical and formulary management professionals. P&T 31, 218–226.Google Scholar
  13. 13.
    13. Hartman, J. M., Forsen, J. W., Wallace, M. S., Neely, J. G. (2002) Tutorials in clinical research, Part IV: Recognizing and controlling bias. Laryngoscope 112, 23–31.PubMedCrossRefGoogle Scholar
  14. 14.
    14. Sitthi-Amorn, C., Poshyachinda, V. (1993) Bias. Lancet 342, 286–288.PubMedCrossRefGoogle Scholar
  15. 15.
    15. Grimes, D. A., Schulz, K. F. (2002) Bias and causal associations in observational research. Lancet 359, 248–252.PubMedCrossRefGoogle Scholar
  16. 16.
    16. Delgado-Rodríez, M., Llorca, J. (2004) Bias. J Epidemiol Community Health 58, 635–641.CrossRefGoogle Scholar
  17. 17.
    17. Major types of research study bias. Available at http://www.umdnj.edu/idsweb/shared/biases.htm.
  18. 18.
    18. Clancy, M. J. (2002) Overview of research designs. Emerg Med J 19, 546–549.PubMedCrossRefGoogle Scholar
  19. 19.
    19. Bayona M., Olsen C. (2004) Observational Studies and Bias in Epidemiology. YES—The Young Epidemiology Scholars Program. Available at http://www.collegeboard.com/prod_downloads/yes/4297_MODULE_19.pdf.
  20. 20.
    20. World Health Organization. (2001) Health Research Methodology: A Guide for Training in Research Methods, 2nd ed. Available at http://www.wpro.who.int/NR/rdonlyres/F334320C-2B19_4F38-A358-27E84FF3BC0F/0/contents.pdf.
  21. 21.
    21. Hammal, D. M., Bell, C. L. (2002) Confounding and bias in epidemiological investigations. Pediatr Hematol Oncol 19, 375–381.PubMedCrossRefGoogle Scholar
  22. 22.
    22. Carayol, J., Khlat, M., Maccario, J., Bonaiti-Pellie, C. (2002) Hereditary non-polyposis colorectal cancer: Current risks of colorectal cancer largely overestimated. J Med Genet 39, 335–339.PubMedCrossRefGoogle Scholar
  23. 23.
    23. Satagopan, J. M., Ben-Porat, L., Berwick, M., Robson, M., Kutler, D., Auerbach, A. D. (2004) A note on competing risks in survival data analysis. Br J Cancer 91, 1229–1235.PubMedCrossRefGoogle Scholar
  24. 24.
    24. Manolio, T. A., Bailey-Wilson, J. E., Collins, F. S. (2006) Genes, environment and the value of prospective cohort studies. Nat Rev Genet 7, 812–820.PubMedCrossRefGoogle Scholar
  25. 25.
    25. Sackett, D. L. (1979) Bias in analytic research. J Chron Dis 32, 51–63.PubMedCrossRefGoogle Scholar
  26. 26.
    26. Dorak, M. T. Bias and confounding. Available at http://www.dorak.info/epi/bc.html.
  27. 27.
    27. EBM Tool Kit. Clinical Epidemiology Glossary. Available at http://www.med.ualberta.ca/ebm/define.htm.
  28. 28.
    28. Vineis, P., McMichael, A. J. (1998) Bias and confounding in molecular epidemiological studies: Special considerations. Carcinogenesis 19, 2063–2067.PubMedCrossRefGoogle Scholar
  29. 29.
    29. Mother and child health: Research methods. Common pitfalls. Available at http://www.oxfordjournals.org/trope/online/ce_ch13.pdf.
  30. 30.
    30. Okasha, M. (2001) Interpreting epidemiological findings. Student BMJ 9, 324–325.Google Scholar
  31. 31.
    Ravani, P., Parfrey, P.S., Dicks, E., Barrett, B. J. (2007) Clinical research of kidney diseases, II: Problems of study design. Nephrol Dial Transpl 22, 2785–2794.CrossRefGoogle Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Health Sciences CentreNewfoundlandCanada

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