time_series: Generate Time Series from Defined Analysis or Analyses
In mds: Medical Devices Surveillance

Description Usage Arguments Value Methods (by class) Examples

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.

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, ...)

`analysis`	A defined analysis object of class `mds_da`, list of class `mds_das`, or a list of objects each of class `mds_da`, usually created by `define_analyses()`.
`...`	Further arguments for future work.
`deviceevents`	A device-event data frame of class `mds_de`, usually created by `deviceevent()`. This should be the same data frame used to generate `analysis`.
`exposure`	Optional exposure data frame of class `mds_e`, usually created by `exposure()`. This should be the same data frame used to generate `analysis`, if exposure data was used. Default: `NULL` will not consider exposure data.
`use_hierarchy`	Deprecated - do not use. Logical value indicating whether device and event hierarchies should be used in counting contingency tables for disproportionality analysis.

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.

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

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)