A set of core functions for handling medical device event data in the context of post-market surveillance, pharmacovigilance, signal detection and trending, and regulatory reporting. Primary inputs are data on events by device and data on exposures by device. Outputs include: standardized device-event and exposure datasets, defined analyses, and time series.
Medical device event data are messy.
Common challenges include:
mds package provides a standardized framework to address these challenges:
Rfiles for auditability, documentation, and reproducibility
Note on Statistical Algorithms
mds data and analysis standards allow for seamless application of various statistical trending algorithms via the
mdsstat package (under development).
The general workflow to go from data to trending over time is as follows:
deviceevent()to standardize device-event data.
exposure()to standardize exposure data (optional).
define_analyses()to enumerate possible analysis combinations.
time_series()to generate counts (and/or rates) by time based on your defined analyses.
library(mds)# Step 1 - Device Eventsde <- deviceevent(maude,time="date_received",device_hierarchy=c("device_name", "device_class"),event_hierarchy=c("event_type", "medical_specialty_description"),key="report_number",covariates="region",descriptors="_all_")# Step 2 - Exposures (Optional step)ex <- exposure(sales,time="sales_month",device_hierarchy="device_name",match_levels="region",count="sales_volume")# Step 3 - Define Analysesda <- define_analyses(de,device_level="device_name",exposure=ex,covariates="region")# Step 4 - Time Seriests <- time_series(da,deviceevents=de,exposure=ex)
plot(ts[], "rate", type='l')