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Approaches to Evaluating Causation of Suspected Drug Reactions

  • Judith K. Jones
Chapter
Part of the NATO ASI Series book series (NSSA, volume 224)

Abstract

Knowledge about an adverse drug effect typically starts out as a signal of a possible problem, usually identified through spontaneous reports of a suspected adverse drug reaction. As diagrammed in Figure 1, this signal is verified in an iterative fashion, either through further reports or studies of its biological plausibility and, if important, it is quantitated with epidemiological or, rarely, experimental studies (clinical trials). Although often taken for granted, the driving force for this iteration is often based on the assumption or determination, through some means, that there is a cause-effect relationship between the purported risk and the pharmaceutical. This qualitative part of risk assessment which evaluates a signal is an important initial part of the process.

Keywords

Adverse Drug Reaction Spontaneous Report Drug Info Biological Plausibility Causality Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    A.R. Feinstein, Clinical Biostatistics. XLVII. Scientific Standards vs. statistical associations and biologic logic in the analysis of causation, Clin Pharmacol Ther. 25:481 (1979).PubMedGoogle Scholar
  2. 2.
    N. Irey, Diagnostic problems in drug-induced diseases, p. 1., in: Drug-Induced Diseases Vol. 4, L. Meylerl, H.M. Peck (eds), Elsevier Science Publishers, Amsterdam (1972).Google Scholar
  3. 3.
    F. Karch, and L. Lasagna, Toward the operational identification of adverse drug reactions, Clin Pharmacol Ther. 21:247 (1977).PubMedGoogle Scholar
  4. 4.
    D.L. Lane, A probabilist’s view of Causality Assessment, Drug Info J. 18:323 (1984).Google Scholar
  5. 5.
    W.M. Turner, The Food and Drug Administration algorithm, Drug Info J. 18:259 (1984).Google Scholar
  6. 6.
    M.S. Kramer, J.M. Levanthal, T.A. Hutchinson, and et. al., An algorithm for the operation al assessment of adverse drug reactions, I. Background, description and instructions for use, J Am Med Assoc. 242:623 (1979).CrossRefGoogle Scholar
  7. 7.
    J. Venulet, A.G. Ciucci, G.C. Berneker, Updating of a method for causality assessment of adverse drug reactions, Int J Clin Pharmacol 24:559 (1986).Google Scholar
  8. 8.
    C.A. Naranjo, U. Busto, E.M. Sellers, and et al., A method for estimating the probability of adverse drug reactions, Clin Pharmacol Ther. 30:239 (1981).PubMedCrossRefGoogle Scholar
  9. 9.
    B. Bégaud, J.C. Evreux, J. Jouglard and et. al., Unexpected or toxic drug reaction assessment (imputation). Actualization of the method used in France, Therapie. 40:111, (1985).PubMedGoogle Scholar
  10. 10.
    J. Venulet, G.C. Bernaker, and A.G. Ciucci, eds. Assessing Causes of Adverse Drug Reactions, Academic Press, London (1982).Google Scholar
  11. 11.
    R. Herman, (ed), Drug Event Associations: Perspectives, Methods, and Users, Proceedings of the Drug Information Workshop, Arlington, VA, October–November 1983, Drug Info J. 18:1 (1984).Google Scholar
  12. 12.
    J.K. Jones, Drug-event Associations: A view of the current status. Epilogue, Drug Info J. 18:331 (1984).Google Scholar
  13. 13.
    J.K. Jones, and R.L. Herman, (eds), The Future of Adverse Drug Reaction Diagnosis: Computers, Clinical Judgment and the Logic of Uncertainty, Proceedings of the Drug Information Assocation Workshop, Arlington, VA, February 1986, Drug Info J. 20 (1986).Google Scholar
  14. 14.
    D.A. Lane, M.S. Kramer, T.A. Hutchinson, and et. al., The Causality Assessment of Adverse Drug Reactions using a Bayesian Approach, Pharmaceutical Med. 2:265 (1987).Google Scholar
  15. 15.
    K.L. Lanctot, and C.A. Naranjo, Using microcomputers to simplify the Bayesian Causality Assessment of Adverse Drug Reactions, Pharmaceut Med. 4:185 (1990).Google Scholar
  16. 16.
    C.A. Naranjo, K.L. Lanctot, and D.A. Lane, Bayesian differential diagnosis of neutropenia associated with antiarrhythmic agents, J Clin Pharmacol 30:12990 (in press).Google Scholar
  17. 17.
    R. Royer, Personal communication.Google Scholar
  18. 18.
    A.P.W.I., Newsletter No. 1 (1989).Google Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Judith K. Jones
    • 1
  1. 1.The Degge Group, Ltd.Georgetown UniversityUSA

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