Effect of reporting bias in the analysis of spontaneous reporting data.
It is well-known that a spontaneous reporting system suffers from significant under-reporting of adverse drug reactions from the source population. The existing methods do not adjust for such under-reporting for the calculation of measures of association between a drug and the adverse drug reaction under study. Often there is direct and/or indirect information on the reporting probabilities. This work incorporates the reporting probabilities into existing methodologies, specifically to Bayesian confidence propagation neural network and DuMouchel's empirical Bayesian methods, and shows how the two methods lead to biased results in the presence of under-reporting. Considering all the cases to be reported, the association measure for the source population can be estimated by using only exposure information through a reference sample from the source population.
Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore.
Palash
P
Ghosh
Anup
A
Dewanji
D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards* / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis / Q000453:epidemiology; D006801:Humans; D016571:Neural Networks, Computer; D013223:Statistics as Topic / Q000379:methods / Q000592:standards*
BCPNN; EBGM; adverse drug reaction; reference sample; spontaneous reporting database; under-reporting