Description Usage Arguments Details Value Methods (by class) References Examples
Test on deviceevents using a onelayer BCPNN as proposed by Bate et al (1998), which assumes betadistributed probabilities and a normal approximation of the variance of the information component (IC). From the family of disproportionality analyses (DPA) used to generate signals of disproportionate reporting (SDRs).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 
df 
Required input data frame of class

... 
Further arguments passed onto 
ts_event 
Required if Default: 
analysis_of 
Optional string indicating the English description of what
was analyzed. If specified, this will override the name of the
Default: Example: 
eval_period 
Required positive integer indicating the number of unique times counting in reverse chronological order to sum over to create the 2x2 contingency table. Default: Example: 
null_ratio 
Numeric value representing the null relative reporting
ratio (RR), used with Default: 
conf_interval 
Numeric value between 0 and 1 representing the width of
the IC confidence interval, where the lower bound of the
interval is assessed against the Default: 
quantiles 
Vector of quantiles between 0 and 1. Default: 
cont_adj 
Nonnegative number representing the continuity adjustment to be added to each cell of the 2x2 contingency table. A value greater than 0 allows for contingency tables with a 0 in the D cell to run the algorithm. Adding a continuity adjustment will adversely affect the algorithm estimates, user discretion is advised. See details for more. Default: 
null_ratio
and conf_interval
are used together to establish the
signal criteria. The null_ratio
is conceptually similar to the
relative reporting ratio under a null hypothesis of no signal. Common values
are 1
and, more conservatively (fewer false signals), 2
. The
conf_interval
is the IC confidence interval used to test for a
signal. A value of 0.90
returns the 5
tests if the lower bound exceeds null_ratio
. Effectively,
conf_interval=0.90
conducts a onesided test at the conventional 0.05
alpha level.
cont_adj
provides the option to allow bcpnn()
to proceed running,
however this is done at the user's discretion because there are adverse
effects of adding a positive number to every cell of the contingency table.
By default, bcpnn()
allows 0 in all cells except D.
It has been suggested that 0.5 may be an appropriate value. However, the
user is cautioned that interpretation may be compromised by adding continuity
adjustments.
For parameter ts_event
, in the uncommon case where the
deviceevent count (Cell A) variable is not "nA"
, the name of the
variable may be specified here. Note that the remaining 3 cells of the 2x2
contingency table (Cells B, C, D) must be the variables "nB"
,
"nC"
, and "nD"
respectively in df
. A named character
vector may be used where the name is the English description of what was
analyzed. Note that if the parameter analysis_of
is specified, it will
override this name. Example: ts_event=c("Count of Bone Cement
Leakages"="event_count")
A named list of class mdsstat_test
object, as follows:
Name of the test run
English description of what was analyzed
Named boolean of whether the test was run. The name contains the run status.
A standardized list of test run results: statistic
for the test statistic, lcl
and ucl
for the set
confidence bounds, p
for the pvalue, signal
status, and
signal_threshold
.
The test parameters
The data on which the test was run
mds_ts
: BCPNN on mds_ts data
default
: BCPNN on general data
Bate A, Lindquist M, et al. A Bayesian Neural Network Method for Adverse Drug Reaction Signal Generation. European Journal of Clinical Pharmacology, 1998, 54, 315321.
Ahmed I, Poncet A. PhViD: PharmacoVigilance Signal Detection, 2016. R package version 1.0.8.
Lansner A, Ekeberg Ö. A onelayer feedback artificial neural network with a bayesian learning rule. Int. J. Neural Syst., 1989, 1, 7787.
1 2 3 4 5 6 7 8 9  # Basic Example
data < data.frame(time=c(1:25),
nA=as.integer(stats::rnorm(25, 25, 5)),
nB=as.integer(stats::rnorm(25, 50, 5)),
nC=as.integer(stats::rnorm(25, 100, 25)),
nD=as.integer(stats::rnorm(25, 200, 25)))
a1 < bcpnn(data)
# Example using an mds_ts object
a2 < bcpnn(mds_ts[[3]])

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