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
This is the current list of algorithms available:
| Function | Description |
xbar() | Shewhart x-bar Control Chart with 4 Western Electric Rules |
cusum() | Cumulative Sum Control Chart with 4 Western Electric Rules |
ewma() | Exponentially Weighted Moving Average |
sprt() | Sequential Probability Ratio Test |
prr() | Proportional Reporting Ratio |
ror() | Reporting Odds Ratio |
gps() | Gamma Poisson Shrinker (containing EBGM and EB05) |
bcpnn() | Bayesian Confidence Propagation Neural Network |
cp_mean() | Mean-Shift Changepoint |
poisson_rare() | Poisson Test on Rare Events |
Refer to the package vignette for guided examples.
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