A new method for assessing drug causation provided agreement with experts' judgment.
OBJECTIVE
The many methods proposed for causality assessment of adverse drug reaction (ADR) generally rely on algorithms. They have no clear relationship to probabilities, however, a situation we attempted to improve.


METHODS
Thirty ADR cases corresponding to 32 suspect drugs were randomly selected from the French pharmacovigilance database. The statistical weighting was performed by using a multilinear regression with logit(p) as the dependent variable and seven judgment criteria as independent variables. The best model (i.e., giving the best correlation with the gold standard) was retained for the new causality assessment method.


RESULTS
The weights [logit(p)] for the 21 choices, on average three for each of the seven criteria, ranged from -3.95 to 0.86, secondarily rounded to multiples of 0.5. The correlation between the probability obtained from the final method and the gold standard was quite good (R(2) = .92).


CONCLUSIONS
This method based on the rational weighting of seven causality criteria is straightforward to use and provides very good agreement with experts' judgment. Moreover, unlike most classical algorithms, it respects one basic rule of probabilities-namely, a symmetrical probability distribution for drug causation around the .5 neutral position (maximum uncertainty).
INSERM U657, 33076 Bordeaux Cedex, France.
Yannick
Y
Arimone
Bernard
B
Bégaud
Ghada
G
Miremont-Salamé
Annie
A
Fourrier-Réglat
Mathieu
M
Molimard
Nicholas
N
Moore
Françoise
F
Haramburu
D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003625:Data Collection; D004347:Drug Interactions; D064420:Drug-Related Side Effects and Adverse Reactions; D005602:France; D006801:Humans; D016014:Linear Models; D011336:Probability; D011358:Product Surveillance, Postmarketing