Bayesian pharmacovigilance signal detection methods revisited in a multiple comparison setting.
Pharmacovigilance spontaneous reporting systems are primarily devoted to early detection of the adverse reactions of marketed drugs. They maintain large spontaneous reporting databases (SRD) for which several automatic signalling methods have been developed. A common limitation of these methods lies in the fact that they do not provide an auto-evaluation of the generated signals so that thresholds of alerts are arbitrarily chosen. In this paper, we propose to revisit the Gamma Poisson Shrinkage (GPS) model and the Bayesian Confidence Propagation Neural Network (BCPNN) model in the Bayesian general decision framework. This results in a new signal ranking procedure based on the posterior probability of null hypothesis of interest and makes it possible to derive with a non-mixture modelling approach Bayesian estimators of the false discovery rate (FDR), false negative rate, sensitivity and specificity. An original data generation process that can be suited to the features of the SRD under scrutiny is proposed and applied to the French SRD to perform a large simulation study. Results indicate better performances according to the FDR for the proposed ranking procedure in comparison with the current ones for the GPS model. They also reveal identical performances according to the four operating characteristics for the proposed ranking procedure with the BCPNN and GPS models but better estimates when using the GPS model. Finally, the proposed procedure is applied to the French data.
Ahmed
Ismaïl
I
Inserm, U780, 16 Avenue Paul Vaillant Couturier, Villejuif F-94807, France. ismail.ahmed@inserm.fr
Haramburu
Françoise
F
Fourrier-Réglat
Annie
A
Thiessard
Frantz
F
Kreft-Jais
Carmen
C
Miremont-Salamé
Ghada
G
Bégaud
Bernard
B
Tubert-Bitter
Pascale
P
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D001699:Biometry; D016208:Databases, Factual; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted