**A. Appendix 1. Details of the algorithm performed**

In this analysis, the algorithm was performed with the following arguments: value of the relative risk (RR) proven to be higher than 1 (RR < 1); minimum number of cases per pair (drug-adverse reaction) to be potentially considered as a signal (N = 1); rule of decision for the generation of signals: false discovery rate (FDR); limit or threshold for the decision rule: FDR > 0.05; statistics used for ordering the drug-ADR pairs: posterior probability of the null hypothesis (post.H0); and calculation of the distribution of the statistic of interest: by approximation to the normal distribution [1a, 2a] and using empirical estimation through Monte Carlo simulations (NB. MC = 10,000) [3a]. The estimator of FDR < 0.05 and specificity (Sp) ≥0.99 are considered to interpret the results. Sensitivity (Se) values are typically low in the BCPNN approach [4a], Se ≥ 0.20 is considered as reference.

The estimator FDR assures that at least 95% of the signals detected are positive (only 5% of false positives). Moreover, if the estimator of false negative rate (FNR) is 50% or lower, it implicates that, at least, half of the signals rejected are effectively negative. In the results presented, all the FNRs were lower than 49%.

[1a] Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol. 1998; doi:10.1007/ s002280050466.

[2a] Gould AL. Practical pharmacovigilance analysis strategies. Pharmacoepidemiol Drug Saf. 2003; doi:10.1002/pds.771.

[3a] Nóren N, editors. A Monte Carlo Method for Bayesian Dependency Derivation. Gothenburg: Chalmers University of Technology; 2002.

[4a] Tada K, Maruo K, Isogawa N, Yamaguchi Y, Gosho M. Borrowing external information to improve Bayesian confidence propagation neural network. Eur J Clin Pharmacol. 2020; doi:10.1007/s00228-020-02909-w.

*Early Signal Detection: Data Mining of Mental Disorders with Statins DOI: http://dx.doi.org/10.5772/intechopen.105504*
