Table 2.

Four different types of disproportionality analysis.

Algorithms	Equation	Criteria
ROR	ROR = (a/c)/(b/d) 95%CI = eln(ROR)±1.96(1/a+1/b+1/c+1/d)^0.5	95 % CI (lower limit) > 1, a ≥3
PRR	PRR = [a/(a+b)]/[c/(c + d)] 95%CI = eln(PRR)±1.96[1/a−1/(a+b)+1/c−1/(c + d)]^0.5	95 % CI (lower limit) > 1, a ≥3
BCPNN	IC = log2a (a+b + c + d)/((a+c) (a+b)) IC025 = eln(IC)−1.96(1/a+1/b+1/c+1/d)^0.5	IC025 > 0, a ≥3
MGPS	EBGM = a (a+b + c + d)/((a+c) (a+b)) EBGM05 = eln(EBGM)−1.64(1/a+1/b+1/c+1/d)^0.5	EBGM05 > 2, a＞0

Notes: Equation: a, number of reports containing both the target drug and the target adverse drug reaction; b, number of reports containing other adverse drug reactions of the target drug; c, number of reports containing the target adverse drug reaction of other drugs; d, number of reports containing other drugs and other adverse drug reactions. The MGPS employs an empirical Bayesian approach, whereby maximum likelihood estimates obtain a prior distribution, and the prior and likelihood are combined to obtain a posterior distribution. The fifth percentile of the posterior distribution is denoted by “EBGM05” and is interpreted as the one-sided 95 % confidence lower bound for the EBGM. Abbreviations: 95 % CI, 95 % confidence interval; N, the number of reports; BCPNN, Bayesian confidence propagation neural network; IC, information component; IC025, the lower limit of the 95 % CI of the IC; EBGM, empirical Bayesian geometric mean; EBGM05, empirical Bayesian geometric mean lower 95 % CI for the posterior distribution.