README.md
In mds: Medical Devices Surveillance

Why Use `mds`?

Medical device event data are messy.

Common challenges include:

Performing ongoing surveillance on messy data
Quickly answering simple questions such as:
- Are events trending up?
- How did my trends look 1 year ago? 2 years ago?
Incompatibility of various sources of device-events
Difficulty integrating exposures, a.k.a. denominator data
Understanding all the possible combinations to analyze
Application of disproportionality analysis (DPA)
Documentation of analyses in a auditable, reproducible way

How Do I Use `mds`?

The mds package provides a standardized framework to address these challenges:

Standardize events involving medical devices
Standardize exposures of the device (also known as opportunities for an event to occur, or event denominator)
Enumerate possible analyses in a flexible way
Generate times series of analyses for trending over time
Set up analyses for easy application of disproportionality analysis (DPA)
Save all files in lightweight R files 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:

Use deviceevent() to standardize device-event data.
Use exposure() to standardize exposure data (optional).
Use define_analyses() to enumerate possible analysis combinations.
Use time_series() to generate counts (and/or rates) by time based on your defined analyses.

library(mds)

# Step 1 - Device Events
de <- 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 Analyses
da <- define_analyses(
  de,
  device_level="device_name",
  exposure=ex,
  covariates="region")

# Step 4 - Time Series
ts <- time_series(
  da,
  deviceevents=de,
  exposure=ex)