ror: Reporting Odds Ratio
In mdsstat: Statistical Trending for Medical Devices Surveillance

Description Usage Arguments Details Value Methods (by class) References Examples

Test on device-events using the reporting odds ratio (ROR). From the family of disproportionality analyses (DPA) used to generate signals of disproportionate reporting (SDRs).

ror(df, ...)

## S3 method for class 'mds_ts'
ror(df, ts_event = c(Count = "nA"), analysis_of = NA, ...)

## Default S3 method:
ror(
  df,
  analysis_of = NA,
  eval_period = 1,
  null_ratio = 1,
  alpha = 0.05,
  cont_adj = 0,
  ...
)

`df`	Required input data frame of class `mds_ts` or, for generic usage, any data frame with the following columns: time Unique times of class `Date` nA Cell A count (class `numeric`) of the 2x2 table: device/event of interest. nB Cell B count (class `numeric`) of the 2x2 table: device/non-event of interest. nC Cell C count (class `numeric`) of the 2x2 table: non-device/event of interest. nD Cell D count (class `numeric`) of the 2x2 table: non-device/non-event of interest.
`...`	Further arguments passed onto `ror` methods
`ts_event`	Required if `df` is of class `mds_ts`. Named string indicating the variable corresponding to the event count (cell A in the 2x2 contingency table). In most cases, the default is the appropriate setting. See details for alternative options. Default: `c("Count"="nA")` corresponding to the event count column in `mds_ts` objects. Name is generated from `mds_ts` metadata.
`analysis_of`	Optional string indicating the English description of what was analyzed. If specified, this will override the name of the `ts_event` string parameter. Default: `NA` indicates no English description for plain `df` data frames, or `ts_event` English description for `df` data frames of class `mds_ts`. Example: `"Count of bone cement leakages"`
`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: `1` considers only the most recent time in `df`. Example: `12` sums over the last 12 time periods to create the 2x2 contingency table.
`null_ratio`	Numeric ROR value representing the null hypothesis, used with `alpha` to establish the signal status and the p-value. Default: `1` indicates a null hypothesis of ROR=1 and tests if the actual ROR is greater than 1.
`alpha`	Numeric value representing the statistical alpha used to establish the signal status. Default: `0.05` corresponds to the standard alpha value of 5%.
`cont_adj`	Numeric value 0 or greater 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 0 cells to run the algorithm. A typical non-zero value is 0.5. Default: `0` adds zero to each cell, thus an unadjusted table. If any cell of the 2x2 is 0, the algorithm will not run.

For parameter ts_event, in the uncommon case where the device-event 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:

test_name: Name of the test run
analysis_of: English description of what was analyzed
status: Named boolean of whether the test was run. The name contains the run status.
result: A standardized list of test run results: statistic for the test statistic, lcl and ucl for the set confidence bounds, p for the p-value, signal status, and signal_threshold.
params: The test parameters
data: The data on which the test was run

mds_ts: ROR on mds_ts data
default: ROR on general data

Stricker BH, Tijssen JG. Serum sickness-like reactions to cefaclor. J Clin Epidemiol. 1992;45(10):1177-84.

Bohm R, Klein H.-J. (v2018-10-16). Primer on Disportionality Analysis. OpenVigil http://openvigil.sourcefourge.net/doc/DPA.pdf

# 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 <- ror(data)
# Example using an mds_ts object
a2 <- ror(mds_ts[[3]])