mdsstat (version 0.3.2)

lrt: Likelihood Ratio Rest

Description

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).

Usage

lrt(df, ...)
# S3 method for mds_ts
lrt(df, ts_event = c(Count = "nA"), analysis_of = NA, ...)
# S3 method for default
lrt(
  df,
  analysis_of = NA,
  eval_period = 1,
  alpha = 0.05,
  mc_sample = 10000,
  ...
)

Arguments

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.

Value

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

Methods (by class)

• mds_ts: LRT on mds_ts data

• default: LRT on general data

Details

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")

References

Huang L, Zalkikar J, Tiwari RC. A Likelihood Ratio Test Based Method for Signal Detection with Application to FDA<U+2019>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.

Examples

# NOT RUN {
# 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]])

# }