Arimone Yannick, Bégaud Bernard, Miremont-Salamé Ghada, Fourrier-Réglat Annie, Molimard Mathieu, Moore Nicholas, Haramburu Françoise
INSERM U657, 33076 Bordeaux Cedex, France.
J Clin Epidemiol. 2006 Mar;59(3):308-14. doi: 10.1016/j.jclinepi.2005.08.012.
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.
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.
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).
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).
针对药物不良反应（ADR）因果关系评估所提出的众多方法通常依赖于算法。然而，这些方法与概率并无明确关联，我们试图改善这种情况。
从法国药物警戒数据库中随机选取30例对应32种可疑药物的ADR病例。通过使用以logit(p)作为因变量、七个判断标准作为自变量的多元线性回归进行统计加权。新的因果关系评估方法保留了最佳模型（即与金标准具有最佳相关性的模型）。
21种选择的权重[logit(p)]，七个标准中每个标准平均有三个权重，范围从-3.95至0.86，四舍五入为0.5的倍数。最终方法得出的概率与金标准之间的相关性相当好（R² = 0.92）。
这种基于对七个因果关系标准进行合理加权得出的方法使用简便，与专家判断高度一致。此外，与大多数经典算法不同，它遵循概率的一个基本规则，即在0.5中性位置（最大不确定性）周围药物因果关系的对称概率分布。
