BCPNN {PhViD} | R Documentation |
Bayesian confidence propagation neural network
Description
Bayesian confidence propagation neural network (Bate et al. 1998, Noren et al. 2006) extended to the multiple comparison framework.
Usage
BCPNN(DATABASE, RR0 = 1, MIN.n11 = 1, DECISION = 1, DECISION.THRES = 0.05,
RANKSTAT = 1, MC = FALSE, NB.MC = 10000)
Arguments
DATABASE |
Object returned by the function |
RR0 |
Value of the tested risk. By default, |
MIN.n11 |
Minimum number of notifications for a couple to be potentially considered as a signal. By default, |
DECISION |
Decision rule for the signal generation based on 1 = FDR (Default value) 2 = Number of signals 3 = Ranking statistic. See |
DECISION.THRES |
Threshold for |
RANKSTAT |
Statistic used for ranking the couples: 1 = Posterior probability of the null hypothesis 2 = 2.5% quantile of the posterior distribution of IC. |
MC |
If |
NB.MC |
If |
Details
The BCPNN method is based on the calculation of the Information Component IC. If MC = FALSE
, the bayesian model used is the beta-binomial proposed by Bate et al. (1998). The statistic of interest (see RANKSTAT
) is calculated by the normal approximation made in Bate et al. (1998) with the use of the exact expectation and variance proposed by Gould (2003). If MC = TRUE
, the model is based on the Dirichlet-multinomial model proposed more recently in Noren et al. (2006). In this case, the statistic of interest is calculated by Monte Carlo simulations.
Value
ALLSIGNALS |
Data.frame summarizing the results of all couples with at least |
SIGNALS |
Same Data.frame as |
NB.SIGNALS |
Number of generated signals. |
INPUT.PARAM |
Parameters entered in the function. |
Author(s)
Ismaïl Ahmed & Antoine Poncet
References
Ahmed I, Haramburu F, Fourrier-Réglat A, Thiessard F, Kreft-Jais C, Miremont-Salamé G, Bégaud B, Tubert-Bitter P. Bayesian pharmacovigilance signal detection methods revisited in a multiple comparison setting. Stat Med. 2009 Jun 15;28(13):1774-1792.
Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A, De Freitas RM, A Bayesian Neural Network Method for Adverse Drug Reaction Signal Generation European Journal of Clinical Pharmacology, 1998, 54, 315-321.
Gould AL, Practical Pharmacovigilance Analysis Strategies Pharmacoepidemiology and Drug Safety, 2003, 12, 559-574
Noren, GN, Bate A, Orre R, Edwards IR, Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events Statistics in Medicine, 2006, 25, 3740-3757.
Examples
## start
data(PhViDdata.frame)
PhViDdata <- as.PhViD(PhViDdata.frame)
# res <- BCPNN(PhViDdata)
## end