Standardizing Medical Devices Surveillance Data

Live Example

# 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)

Summarize Defined Analyses

summary(da)
#> $`Analyses Timestamp`
#> [1] "2020-06-14 21:45:56 EDT"
#> 
#> $`Analyses Counts`
#>         Total Analyses Analyses with Exposure          Device Levels 
#>                     27                     27                      6 
#>           Event Levels             Covariates 
#>                      1                      2 
#> 
#> $`Date Ranges`
#>           Data      Start        End
#> 1 Device-Event 2017-01-01 2017-12-01
#> 2     Exposure 2017-01-01 2017-12-01
#> 3         Both 2017-01-01 2017-12-01

Show All Analyses as a Data Frame

dadf <- define_analyses_dataframe(da)

head(dadf, 3)

Plot Time Series of Counts and Rates

plot(ts[[1]])
plot(ts[[4]], "rate", type='l')

Required Arguments

data_frame

is the input device-event data frame. All remaining arguments refer to variables within this data frame.

time

is the name of the variable containing the time of the event. The input format of the time will be flexibly converted into Date format.

device_hierarchy

organizes all your device variables into a hierarchy. The hierarchical concept reflects how devices are often nested into progressively more general groups. Set the first variable as the lowest device level that you would like to trend at. mds remembers this hierarchy and allows trending at multiple levels as you specify.

event_hierarchy

organizes all your event variables into a hierarchy. Like devices, event variables should be categorical in nature. Free text descriptions should not be listed here, but rather in the descriptors argument. The hierarchical concept reflects how events are often nested into progressively more general groups. Set the first variable as the lowest event level that you would like to trend at. mds remembers this hierarchy and allows trending at multiple levels as you specify. If your data does not have an event variable, you will need to create a dummy variable.

Optional Arguments

key

is a unique identifier for each unique event in data_frame. If your data pipeline carries over a key variable, it is recommended to specify it here. The key allows downstream aggregated analysis to be able to “look up” individual constituent events.

covariates

are a special group of variables that may be analyzed within device. For instance, declaring covariates="Region" will allow analysis of regions within device. These variables should be categorical in nature.

descriptors

are additional variables that should be retained for the purpose of describing individual events in downstream analysis.

implant_days

contains the age in days of an implantable device at the time of the event.

Required Arguments

Note: Although not required, count will commonly be used as well.

data_frame

is the input exposure data frame. All remaining arguments refer to variables within this data frame.

time

is the name of the variable containing the time of the exposure. The input format of the time will be flexibly converted into Date format. If exposure will be used, it is critical to have sufficient time granularity. For example, if analysis will be done monthly, exposure data must be no less granular than monthly. mds does not make assumptions about filling in holes in time!

device_hierarchy

contains all exposure device variables to match to your device-event data. As such, the values within these variables must match exactly to the values within each respective variable in the device-event data device_hierarchy parameter.

event_hierarchy

contains all exposure event variables to match to your device-event data. As such, the values within these variables must match exactly to the values within each respective variable in the device-event data event_hierarchy parameter. Exposures at an event level is not common.

Optional Arguments

count

is the most commonly specified optional parameter. It contains the number of exposures. If not specified, the number of rows will be used as a proxy for count.

key

is a unique identifier for each unique exposure in data_frame. If your data pipeline carries over a key variable, it is recommended to specify it here. The key allows downstream aggregated analysis to be able to “look up” individual constituent exposure records.

match_levels

are variables aside from time, device, and event that specify an exposure. A common “match level” is country, if your exposure data is specific by country.

Required Arguments

deviceevents

is a standardized device-event data frame. (class() should contain "mds_de")

device_level

is the source device variable to analyze by. Access these variable names by submitting attributes(de)$device_hierarchy.

Optional Arguments

event_level

is the source event variable to analyze by. Access these variable names by submitting attributes(de)$event_hierarchy.

exposure

is a standardized exposure data frame. (class() should contain "mde_e")

date_level and date_level_n

are arguments used together to specify the time interval to analyze by. Default of "months" and 1 analyzes by month. Other examples include "months" and 12 for yearly, or "days" and 7 for weekly.

covariates

are variables to analyze within device. For example, c("region") analyzes by each level of region within device.

times_to_calc

specifies how many time periods (counting back in time) to analyze for. Time period is defined using date_level and date_level_n.

What About Analyses Across All Events, All Devices, etc?

It is always assumed that analyses at aggregated levels are desired. (such as analysis of all events for a given device, or analysis of all events across all devices)

Aggregated level analysis is easily recognized by the "All" and "Data" values in device_level, event_level, covariate, and covariate_level.

How to Customize Analyses

There are several options:

• Select certain analyses from the list (e.g. da[[c(1:5, 24:27)]]))
• Rerun define_analyses() with different parameter settings
• Edit values within an individual analysis (e.g. da[[1]]$date_range_exposure['start'] <- as.Date("2016-10-01"))

Required Arguments

analysis

is a single defined analysis (class() should contain "mds_da") or a list of defined analysis.

deviceevents

is a standardized device-event data frame (class() contains "mds_de"). It is typically the same data frame used to generate analysis, but can be another "mds_de" data frame, such as a cut of the data at a different time. Note if, say, an older dataset is being used, the analysis date ranges must correspond.

Optional Arguments

exposure

is a standardized exposure data frame (class() contains "mds_e"). It is typically the same data frame used to generate analysis. Like deviceevents, another data frame may be used, but the analysis instructions must correspond.

use_hierarchy

is a logical value for whether device and event hierarchies should be used for the calculation of disproportionality analysis (DPA) counts. Submit ?time_series.mds_da for more details.

How to Modify Counts & Exposures

It is not uncommon to adjust event and exposure counts, such as with applications of rolling or moving averages. These adjustments should be applied after generating time series data frames from time_series().