A collection of common statistical algorithms used in active surveillance of medical device events. Context includes post-market surveillance, pharmacovigilance, signal detection and trending, and regulatory reporting. Primary inputs are device-event time series. Outputs include trending results with the ability to run multiple algorithms at once. This package works well with the 'mds' package, but does not require it.
There are many ways to trend medical device event data. Some are drawn from the quality control discipline, others from disproportionality analysis used in pharmacoepidemiology, and yet others from the general field of statistics.
There is a need to rigorously compare and contrast these various methods to more fully understand their respective performance and applicability in surveillance of medical devices.
mdsstat package aims to provide a collection of statistical trending algorithms used in medical device surveillance. Furthermore, each algorithm is written with a standardized, reusable framework philosophy. The same input data can be fed through multiple algorithms. All algorithms return results that can be sorted, stacked, and compared.
This package is written in tandem with the
mds package. These are complementary in the sense that:
mdsstandardizes medical device event data.
mdsstatstandardizes the statistical trending of medical device event data.
mdsstat algorithms can run on generic R data frames, additional efficiency and traceability benefits are derived by running on data frames generated by
mds::time_series() from the
Refer to the package vignette for available algorithms and guided examples.
run_algos(): add parameter to skip/warn/stop when trying to run DPA on non-DPA time series
run_algos()to run on a list of
mds_tsor other properly formatted objects
shewhart()now outputs the correct signal logic
prr()now outputs the correct signal logic and p-value