lrt: Likelihood Ratio Rest
In mdsstat: Statistical Trending for Medical Devices Surveillance

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

Test on device-events using the Likelihood Ratio Test, originally proposed by Huang & Tiwari (2011). From the family of disproportionality analyses (DPA) used to generate signals of disproportionate reporting (SDRs).

lrt(df, ...)

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

## Default S3 method:
lrt(
  df,
  analysis_of = NA,
  eval_period = 1,
  alpha = 0.05,
  mc_sample = 10000,
  ...
)

`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 `lrt` 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.
`alpha`	Alpha or Type-I error rate in the range (0, 1), used to determine signal status. It is the threshold for determining if the observed reporting rate is greater than the expected based on Monte Carlo simulations of the null. Default: `0.05` is an alpha level of 0.05 or 5%.
`mc_sample`	Number of Monte Carlo samples for constructing the null distribution based on empirical data. Lowest recommended is 1000. Increasing iterations also increases p-value precision. Default: `10000` uses 10000 bootstrap iterations.

This is an implementation of the "Regular LRT" per Huang & Tiwari (2019). It assumes a test on a single event of interest where all other events & devices are collapsed, effectively testing a 2x2 table only. Therefore this is a test on the significance of the likelihood ratio instead of the maximum likelihood over i events for a given medical product j (refer to Huang & Tiwari, 2011).

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: LRT on mds_ts data
default: LRT on general data

Huang L, Zalkikar J, Tiwari RC. A Likelihood Ratio Test Based Method for Signal Detection with Application to FDA’s Drug Safety Data. Journal of the American Statistical Association, 2011, Volume 106, Issue 496, 1230-1241.

Huang L, Zalkikar J, Tiwari RC. Likelihood-Ratio-Test Methods for Drug Safety Signal Detection from Multiple Clinical Datasets. Comput Math Methods Med. 2019, PMC6399568.

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