Adherence to Medications
callAdhereR.
CMA_per_episode constructor.
CMA constructor for polypharmacy.
CMA_sliding_window constructor.
CMA0 constructor.
CMA1 and CMA3 constructors.
CMA2 and CMA4 constructors.
CMA5 constructor.
CMA6 constructor.
CMA7 constructor.
CMA8 constructor.
CMA9 constructor.
Computation of event durations.
Gap Days and Event (prescribing or dispensing) Intervals.
Compute Treatment Episodes.
Cover special periods.
Example dispensing events for 16 patients.
Example special periods for 10 patients.
Example prescription events for 16 patients.
Get the actual plotting area.
Get the legend plotting area.
Get info about the plotted events.
Get info about the plotted partial CMAs.
getCallerWrapperLocation.
Access the actual CMA estimate from a CMA object.
Access the event info from a CMA object.
getEventsToEpisodesMapping
getEventsToSlidingWindowsMapping
Access the inner event info from a complex CMA object.
Access the medication groups of a CMA object.
Access last adherence plot info.
Map from event to plot coordinates.
Example of medication events with ATC codes.
Example medication events records for 100 patients.
Example of medication groups.
Interactive exploration and CMA computation.
Plot CMA_per_episode and CMA_sliding_window objects.
Plot CMA0 objects.
Plot CMA0-derived objects.
Print CMA0 (and derived) objects.
Prune event durations.
Restrict a CMA object to a subset of patients.
Computation of initiation times.
Computation of adherence to medications from Electronic Health care Data and visualization of individual medication histories and adherence patterns. The package implements a set of S3 classes and functions consistent with current adherence guidelines and definitions. It allows the computation of different measures of adherence (as defined in the literature, but also several original ones), their publication-quality plotting, the estimation of event duration and time to initiation, the interactive exploration of patient medication history and the real-time estimation of adherence given various parameter settings. It scales from very small datasets stored in flat CSV files to very large databases and from single-thread processing on mid-range consumer laptops to parallel processing on large heterogeneous computing clusters. It exposes a standardized interface allowing it to be used from other programming languages and platforms, such as Python.