Character Vector to Header of Empty Data Frame

Description

Converts a character vector into the column names of an empty data frame. Used in conjunction with check_names parameter of input_param_checker()

Usage

char_to_df(x)

Arguments

Value

Empty data frame where columns are named x.

Convert to Acceptable Date

Description

Converts a Date vector into its equivalent daily, monthly, yearly, etc... Date vector.

Usage

convert_date(x, convert_type = "months", convert_to_n = 1)

Arguments

Value

Converted Date vector of class mdpms.Date with attributes adder (function that adds units), convert_to_n, and convert_type.

Assess Analyses Definitions

Description

Define analyses based on an MD-PMS device-event data frame and, optionally, an MD-PMS exposure data frame. See Details for how to use.

Usage

define_analyses(
  deviceevents,
  device_level,
  event_level = NULL,
  exposure = NULL,
  date_level = "months",
  date_level_n = 1,
  covariates = "_none_",
  times_to_calc = NULL,
  invivo = FALSE,
  prior = NULL
)

Arguments

Details

define_analyses() is a prerequisite to calling time_series(). This function enumerates all possible analyses based on input device-event (deviceevent()) and, optionally, exposure (exposure()) data frames. An analysis is defined as a set of instructions specifying at minimum the device level, event level, the date range of analysis, and the date unit. Additional instructions include the covariate level, time in-vivo status, and exposure levels.

By separating the analysis enumeration (define_analyses()) from the generation of the time series (time_series()), the user may rerun the analyses on different datasets and/or filter the analyses to only those of interest.

The analyses definitions will always include rollup levels for each of device_level, event_level (if specified), and covariates. Rollups are analyses at all device, event, and/or covariate levels. These rollup analyses will be indicated by the keyword 'All' in the analysis definition.

When a hierarchy of 2 or more variables for either device_level or event_level are present in deviceevents, define_analyses() will enforce the 1-level-up parent level ONLY. Additional higher parent levels are not currently enforced, thus the user is advised to uniquely name the 1-level-up parent level. The parent level DOES NOT ROLLUP currently because the parent level is intended to separate disparate data and devices. This may change in the future.

If exposure is specified, any available match_levels will be used to calculate the appropriate timeframe for analyses. The exception are the special rollup analyses (see prior paragraph).

When covariates are specified, a special rollup analysis definition will always be defined that does not consider the covariates at all. This analysis can be identified by covariate='Data' and covariate_level='All' in the output mds_da object.

When covariates are specified and there is no variation in the distribution of covariate values (e.g. all males, all 10, all missing) in the device- and event-specific dataset, these specific analyses will be dropped.

When factor covariates are specified, covariate-level analyses may be defined two ways: 1) detect an overall covariate level effect, also known as a 3-dimensional analysis, and 2) subset the data by each level of the covariate, also known as a subgroup analysis. 1) will be denoted as covariate_level='All' in the output mds_da object, while 2) will specify the factor level in covariate_level.

If invivo=TRUE, define_analyses() will first verify if data exists in the time_invivo variable for the given device_level, event_level, and, if applicable, covariates level. If no data exists, invivo will be implicitly assigned to FALSE.

Value

A list of defined analyses of class mds_das. Each list item, indexed by a numeric key, defines a set of analyses for a unique combination of device, event, and covariate level. Each list item is of the class mds_da. Attributes of class mds_das are as follows:

date_level

Defined value for date_level

date_level_n

Defined value for date_level_n

device_level

Defined value for device_level

event_level

Defined value for event_level

times_to_calc

Defined value for times_to_calc

prior_used

Boolean for whether prior was specified.

timestamp

System time when the analyses were defined.

Examples

# Device-Events
de <- deviceevent(
  data_frame=maude,
  time="date_received",
  device_hierarchy=c("device_name", "device_class"),
  event_hierarchy=c("event_type", "medical_specialty_description"),
  key="report_number",
  covariates=c("region"),
  descriptors="_all_")
# Exposures
ex <- exposure(
  data_frame=sales,
  time="sales_month",
  device_hierarchy="device_name",
  match_levels="region",
  count="sales_volume")
# Defined Analyses - Simple example
da <- define_analyses(de, "device_name")
# Defined Analyses - Simple example with a quarterly analysis
da <- define_analyses(de, "device_name", date_level_n=3)
# Defined Analyses - Example with event type, exposures, and covariates
da <- define_analyses(de, "device_name", "event_type", ex, covariates="region")

Create Data Frame from Analyses Definitions

Description

Returns a data frame summarizing all defined analyses from the mds_das object.

Usage

define_analyses_dataframe(inlist)

Arguments

Value

A data frame with each row representing an analysis.

MD-PMS Device Event Data Frame

Description

Converts a data frame into a MD-PMS Device Event data frame.

Usage

deviceevent(
  data_frame,
  time,
  device_hierarchy,
  event_hierarchy,
  key = NULL,
  covariates = NULL,
  descriptors = NULL,
  time_invivo = NULL
)

Arguments

Details

When more than 2 variables are specified in either device_hierarchy or event_hierarchy, it is important to note that a subsequent call to define_analyses() currently only utilizes a maximum of 2 variables: the lowest level and the 1-level-up parent. The user may enforce full hierarchy in >2 variable cases by ensuring that the parent values are uniquely named.

time_invivo can be thought of more generally as the time of exposure of the device to the subject at the time of the event. The common usage is duration of the implant in the patient at time of event, for an implantable medical device.

Value

A standardized MD-PMS data frame of class mds_de. Rows are deduplicated. Attributes are as follows:

key

Original variable name for key

time

Original variable name for time

device_hierarchy

Vector of original variable names for device_hierarchy with converted variable names correspondingly named.

event_hierarchy

Vector of original variable names for event_hierarchy with converted variable names correspondingly named.

covariates

Vector of original variable names for covariates with converted variable names correspondingly named.

descriptors

Vector of original variable names for descriptors with converted variable names correspondingly named.

Examples

# A barebones dataset
de <- deviceevent(maude, "date_received", "device_name", "event_type")
# With more variables and variable types
de <- deviceevent(
  data_frame=maude,
  time="date_received",
  device_hierarchy=c("device_name", "device_class"),
  event_hierarchy=c("event_type", "medical_specialty_description"),
  key="report_number",
  covariates=c("region"),
  descriptors="_all_")

MD-PMS Exposure Data Frame

Description

Converts a data frame into a MD-PMS Exposure data frame.

Usage

exposure(
  data_frame,
  time,
  device_hierarchy,
  event_hierarchy = NULL,
  key = NULL,
  match_levels = NULL,
  count = NULL
)

Arguments

Value

A standardized MD-PMS data frame of class mds_e. Rows are deduplicated. Attributes are as follows:

key

Original variable name for key

time

Original variable name for time

device_hierarchy

Vector of original variable names for device_hierarchy with converted variable names correspondingly named.

event_hierarchy

Vector of original variable names for event_hierarchy with converted variable names correspondingly named.

match_levels

Vector of variable names for grouping factors

count

Original variable name for count

Examples

# A barebones dataset
ex <- exposure(sales, "sales_month", "device_name")
# With more variables and variable types
ex <- exposure(
  data_frame=sales,
  time="sales_month",
  device_hierarchy="device_name",
  match_levels="region",
  count="sales_volume")

Min/Max With All NA's Allowed Min and Max functions that allows the vector x to be NA, where f is min or max

Description

Min/Max With All NA's Allowed Min and Max functions that allows the vector x to be NA, where f is min or max

Usage

fNA(x, f)

Arguments

Value

Numeric value corresponding to min or max, or NA

Class-Preserving If-Else If-Else function preserving T & F classes

Description

Class-Preserving If-Else If-Else function preserving T & F classes

Usage

ifelse_cp(test, yes, no)

Arguments

Value

A vector of the same length and attributes as test.

Check Input Parameters

Description

Verifies correct class and, optionally, verifies existence in a data frame

Usage

input_param_checker(
  x,
  check_class = NULL,
  check_names = NULL,
  null_ok = T,
  exclusions = NULL,
  max_length = NULL
)

Arguments

Value

Stop error, if an error is found. Else nothing.

Bone Cement MAUDE Events in 2017

Description

A dataset containing 535 events reported into the FDA MAUDE database on bone cement in 2017. Data were obtained via the openFDA API (https://open.fda.gov).

Usage

maude

Format

A data frame with 535 rows and 15 variables. Full variable descriptions may be found on the FDA Device Reference Guide (https://open.fda.gov). Note that region is a simulated variable not present in MAUDE. Descriptions as follows:

report_number

Identifying number for the adverse event report.

event_type

Outcomes associated with the adverse event.

date_received

Date the report was received by the FDA.

product_problem_flag

Indicates whether or not a report was about the quality, performance or safety of a device.

adverse_event_flag

Whether the report is about an incident where the use of the device is suspected to have resulted in an adverse outcome in a patient.

report_source_code

Source of the adverse event report.

lot_number

The lot number found on the label or packaging material.

model_number

The exact model number found on the device label or accompanying packaging.

manufacturer_d_name

Device manufacturer name.

manufacturer_d_country

Device manufacturer country.

brand_name

The trade or proprietary name of the suspect medical device as used in product labeling or in the catalog.

device_name

This is the proprietary name, or trade name, of the cleared device.

medical_specialty_description

Regulation Medical Specialty is assigned based on the regulation (e.g. 21 CFR Part 888 is Orthopedic Devices).

device_class

A risk based classification system for all medical devices ((Federal Food, Drug, and Cosmetic Act, section 513)

region

A simulated, randomly assigned geographical region for package example purposes.

Source

https://open.fda.gov/data/maude/

Example List of mds_ts Time Series Objects

Description

An example list of time series objects (class mds_ts) generated using the mds package.

Usage

mds_ts

Format

A list of 3 elements each of class mds_ts

Source

See ?maude and ?sales for source device-event and exposure data. See ?mds::time_series for how to generate mds_ts time series.

Return next level up device

Description

Returns the variable name of the next level in the device hierarchy

Usage

next_dev(x)

Arguments

Value

String representation of the next device level variable

Return next level up event

Description

Returns the variable name of the next level in the event hierarchy

Usage

next_ev(x)

Arguments

Value

String representation of the next event level variable

Plot MD-PMS Time Series

Description

Quickly visualizes an MD-PMS times series of class mds_ts.

Usage

## S3 method for class 'mds_ts'
plot(x, mode = "nA", xlab = "Time", ylab = "Count", main = NULL, ...)

Arguments

Details

mode values defined as follows. Note: The following definitions use a device-event pair as a working example, however it may also be a covariate-device pair.

'nA'

Counts of the device-event pair.

'nB'

Counts of the device for all other events.

'nC'

Counts of all other devices for the event.

'nD'

Counts of all other devices for all other events.

'exposure'

Counts of exposure for the device-event pair.

'rate'

A crude rate, calculated as the device-event counts pair divided by the exposure counts.

Simulated Device Sales Data

Description

A dataset containing simulated monthly sales by device and country for devices reported in the maude dataset. For package usage examples, this data serves as a proxy for exposures. Data were generated using a random normal distribution weighted by the number of reported events by device and country.

Usage

sales

Format

A data frame with 360 rows and 4 variables:

device_name

Name of the device mapped from the maude dataset.

region

Geographical region mapped from the maude dataset.

sales_month

The month of sales.

sales_volume

The volume of sales.

Source

Random normal distribution using rnorm().

Summarize a Collection of MD-PMS Defined Analyses Prints basic counts and date ranges by various analysis factors as defined in the original define_analyses() call.

Description

Summarize a Collection of MD-PMS Defined Analyses Prints basic counts and date ranges by various analysis factors as defined in the original define_analyses() call.

Usage

## S3 method for class 'mds_das'
summary(object, ...)

Arguments

Value

List of analyses counts and date ranges.

Generate Time Series from Defined Analysis or Analyses

Description

Creates time series data frame(s) from defined analysis/analyses (define_analyses()), device-event data frame (deviceevent()), and optionally, exposure data frame (exposure()). If analysis includes covariates or time in-vivo, creates the relevant supporting data frame.

Usage

time_series(analysis, ...)

## S3 method for class 'list'
time_series(analysis, ...)

## S3 method for class 'mds_das'
time_series(analysis, ...)

## S3 method for class 'mds_da'
time_series(analysis, deviceevents, exposure = NULL, use_hierarchy = T, ...)

Arguments

Value

A standardized MD-PMS time series data frame of class mds_ts.

The data frame contains, by defined date levels, the following columns:

nA

Count of the device & event level of interest. If covariate analysis is indicated, this will be at the covariate & device level of interest.

nB

Optional. Count of the device & non-event, or if covariate analysis, covariate & non-device. nB will be missing if this is an 'All' level analysis.

nC

Optional. Count of the non-device & event, or if covariate analysis, non-covariate & device. nC will be missing if this is an 'All' level analysis.

nD

Optional. Count of the non-device & non-event, or if covariate analysis, non-covariate & non-device. nD will be missing if this is an 'All' level analysis.

ids

List of all keys from deviceevents constituting nA.

exposure

Optional. Count of exposures applicable to nA. This counts at the device and covariate levels but not at the event level. If a matching device and/or covariate level is not found, then exposure will be NA. The exception is an 'All' level analysis, which counts exposures across all levels.

ids_exposure

Optional. List of all exposure keys from exposure applicable to nA.

The mds_ts class attributes are as follows:

title

Short description of the analysis.

analysis

The analysis definition of class mds_da.

exposure

Boolean of whether exposure counts are present.

dpa

Boolean of whether 2x2 contingency table counts are present (presumably for disproportionality analysis or 'DPA').

dpa_detail

Optional. If dpa is TRUE, list object containing labels for the DPA contingency table.

covar_data

Optional. If analysis definition includes covariate level or time in-vivo, data.frame object containing the relevant data.

Methods (by class)

• list: Generate time series from a list

• mds_das: Generate time series from a list of defined analyses

• mds_da: Generate time series using defined analysis

Examples

de <- deviceevent(maude, "date_received", "device_name", "event_type")
ex <- exposure(sales, "sales_month", "device_name", count="sales_volume")
da <- define_analyses(de, "device_name", exposure=ex)
# Time series on one analysis
time_series(da, de, ex)
# Time series on multiple analyses
time_series(da[1:3], de, ex)