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Agreement of expert judgment in causality assessment of adverse drug reactions

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Global introspection is, with operational algorithms and Bayes’ theorem, one of the three main approaches used to assess the causal relationship between a drug treatment and the occurrence of an adverse event.


To analyze and compare the judgments of five senior experts using global introspection about drug causation on a random set of putative adverse drug reactions.


A random sample of 150 drug-effect pairs was constituted. For each pair, five senior experts had to independently assess the probability of drug causation from 0 to 1 by using a 100 mm visual analog scale (VAS). For analysis, those probabilities were secondarily split into seven levels of causality: excluded (0–0.05); unlikely (0.06–0.25); doubtful (0.26–0.45); unassessable/unclassifiable (0.46–0.55); plausible (0.56–0.75); likely (0.75–0.95); and certain (0.95–1). Agreement among the five experts was assessed using kappa coefficients (κ).


The overall agreement between experts was poor (κ=0.20), although significantly different from chance, and varied according to the level of causality. It was lower for the unlikely, doubtful, unassessable/unclassifiable, and plausible categories (κ=0.03, 0.03, −0.01, and 0.13, respectively) than for VAS extremes: excluded, likely, and certain (κ=0.40, 0.32, and 0.30, respectively).


This study confirms that experts express marked disagreements when assessing drug causality independently. The agreement rate was lower for intermediate levels of causality, especially when strong evidence was lacking for confirming or ruling out drug causality. Therefore, in a decision-making context, a step-by-step consensual approach such as the Delphi method seems necessary to make the assessment of such cases more reliable.

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  1. Meyboom RH, Hekster YA, Egberts AC, Gribnau FW, Edwards IR (1997) Causal or casual? The role of causality assessment in pharmacovigilance. Drug Saf 17:374–389

    Google Scholar 

  2. Stephens MD (1987) The diagnosis of adverse medical events associated with drug treatment. Adverse Drug React Acute Poisoning Rev 6:1–35

    CAS  PubMed  Google Scholar 

  3. Lanctôt KL, Naranjo CA (1995) Comparison of the Bayesian approach and a simple algorithm for assessment of adverse drug events. Clin Pharmacol Ther 58:692–698

    Google Scholar 

  4. Auriche M (1985) Approche bayésienne de l’imputabilité des phénomènes indésirables aux médicaments. Therapie 40:301–306

    Google Scholar 

  5. Kramer MS (1989) Imputabilité des effets indésirables: individu (analyse du cas) versus groupe (épidémiologie). In: 3es entretiens Jacques Cartier, pp 31–44

  6. Bégaud B (2000) Dictionary of pharmacoepidemiology. Wiley, Chichester

    Google Scholar 

  7. Hutchinson TA, Lane DA (1989) Assessing methods for causality assessment of suspected adverse drug reactions. J Clin Epidemiol 42:5–16

    Google Scholar 

  8. Péré JC, Godin MH, Bégaud B, Haramburu F, Albin H (1985) Sensibilité et spécificité des critères d’imputabilité Etude et comparaison de ces indices d’efficacité pour sept méthodes. Therapie 40:307–312

    Google Scholar 

  9. Pouyanne P, Haramburu F, Imbs JL, Bégaud B (2000) Admissions to hospital caused by adverse drug reactions: cross sectional incidence study French Pharmacovigilance Centres. BMJ 320:1036

    Google Scholar 

  10. Hutchinson TA, Flegel KM, HoPingKong H, Bloom WS, Kramer MS, Trummer EG (1983) Reasons for disagreement in the standardized assessment of suspected adverse drug reactions. Clin Pharmacol Ther 34:421–426

    Google Scholar 

  11. Blanc S, Leuenberger P, Berger JP, Brooke EM, Schelling JL (1979) Judgments of trained observers on adverse drug reactions. Clin Pharmacol Ther 25:493–498

    Google Scholar 

  12. Leventhal JM, Hutchinson TA, Kramer MS, Feinstein AR (1979) An algorithm for the operational assessment of adverse drug reactions. III. Results of tests among clinicians. JAMA 242:1991–1994

    Google Scholar 

  13. Karch FE, Smith CL, Kerzner B, Mazzullo JM, Weintraub M, Lasagna L (1976) Adverse drug reactions-a matter of opinion. Clin Pharmacol Ther 19:489–492

    Google Scholar 

  14. Kramer MS (1981) Difficulties in assessing the adverse effects of drugs. Br J Clin Pharmacol 11(Suppl 1):105S–110S

    Google Scholar 

  15. Koch-Weser J, Sellers EM, Zacest R (1977) The ambiguity of adverse drug reactions. Eur J Clin Pharmacol 11:75–78

    Google Scholar 

  16. Huskisson EC (1974) Measurement of pain. Lancet 2:1127–1131

    CAS  PubMed  Google Scholar 

  17. Lagier G, Vincens M, Castot A (1983) Imputability in drug monitoring. Principles of the balanced drug reaction assessment method and principal errors to avoid. Therapie 38:303–318

    Google Scholar 

  18. Miremont G, Haramburu F, Bégaud B, Péré JC, Dangoumau J (1994) Adverse drug reactions: physicians’ opinions versus a causality assessment method. Eur J Clin Pharmacol 46:285–289

    Google Scholar 

  19. Macedo AF, Marques FB, Ribeiro CF, Teixeira F (2003) Causality assessment of adverse drug reactions: comparison of the results obtained from published decisional algorithms and from the evaluations of an expert panel, according to different levels of imputability. J Clin Pharm Ther 28:137–143

    Google Scholar 

  20. Venulet J, Ciucci A, Berneker GC (1980) Standardized assessment of drug-adverse reaction associations–rationale and experience. Int J Clin Pharmacol Ther Toxicol 18:381–388

    Google Scholar 

  21. Feinstein AR (1974) Clinical biostatistics XXX. Biostatistical problems in ’compliance bias’. Clin Pharmacol Ther 16:846–857

    Google Scholar 

  22. Péré JC, Bégaud B, Haramburu F, Albin H (1986) Computerized comparison of six adverse drug reaction assessment procedures. Clin Pharmacol Ther 40:451–461

    Google Scholar 

  23. Graham B, Regehr G, Wright JG (2003) Delphi as a method to establish consensus for diagnostic criteria. J Clin Epidemiol 56:1150–1156

    Google Scholar 

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This study was funded as a research project by a grant from the non-profit-making association, ARME-Pharmacovigilance (Bordeaux, France) and supported by the Agence Françoise de Sécurité Sanitaire des Produits de Santé. We gratefully acknowledge the contributions of Jacques Caron (Centre régional de Pharmacovigilance, Lille), Georges Lagier (Centre régional de Pharmacovigilance, Paris Fernand Widal), Louis Merle, (Centre régional de Pharmacovigilance, Limoges) and Thierry Vial (Centre régional de Pharmacovigilance, Lyon).

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Correspondence to Yannick Arimone.

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Arimone, Y., Bégaud, B., Miremont-Salamé, G. et al. Agreement of expert judgment in causality assessment of adverse drug reactions. Eur J Clin Pharmacol 61, 169–173 (2005).

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