Type: Package
Title: Assess Medication Adherence from Pharmaceutical Claims Data
Version: 1.0.0
Date: 2020-10-27
Description: A (mildly) opinionated set of functions to help assess medication adherence for researchers working with medication claims data. Medication adherence analyses have several complex steps that are often convoluted and can be time-intensive. The focus is to create a set of functions using "tidy principles" geared towards transparency, speed, and flexibility while working with adherence metrics. All functions perform exactly one task with an intuitive name so that a researcher can handle details (often achieved with vectorized solutions) while we handle non-vectorized tasks common to most adherence calculations such as adjusting fill dates and determining episodes of care. The methodologies in referenced in this package come from Canfield SL, et al (2019) "Navigating the Wild West of Medication Adherence Reporting in Specialty Pharmacy" <doi:10.18553/jmcp.2019.25.10.1073>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports: Rcpp (≥ 1.0.5), anytime, tidyr, dplyr, purrr, lubridate, rlang
LinkingTo: Rcpp
RoxygenNote: 7.1.0
Depends: R (≥ 2.10)
LazyData: true
Suggests: testthat, spelling
URL: https://github.com/btbeal/adheRenceRX
BugReports: https://github.com/btbeal/adheRenceRX/issues
Language: en-US
NeedsCompilation: yes
Packaged: 2020-11-19 18:09:29 UTC; btbeal
Author: Brennan Beal [aut, cre]
Maintainer: Brennan Beal <brennanbeal@gmail.com>
Repository: CRAN
Date/Publication: 2020-11-20 10:40:10 UTC

A Toy Claims tibble for Performance Demonstration

Description

A toy pharmaceutical claims data set meant to be used to benchmark other algorithms

Usage

big_data_toy

Format

A tibble with 100,000 rows and 6 variables:

ID

patient ID to be grouped by

date

date of claim

days_supply

number of days supplied

Calculate Proportion Days Covered

Description

Calculate the proportion of days covered (PDC) from a pharmaceutical claims database. This function is suggested only after one has properly adjusted their dates (propagate_date()) and identified gaps in therapy (identify_gaps()). This function calculates a length of total therapy as the first fill date to the last for a given grouping. Finally, if you'd like to view adherence by episodes after you have used rank_episodes(), the function will re-adjust gaps for you so that the gap that defined the episode isn't included.

Usage

calculate_pdc(.data, .summarise = TRUE)

Arguments

.data

data frame

.summarise

Logical value (defaulting to TRUE) indicating whether the output should be summarised or not

Value

a summarised tibble, by default, with proportion of days covered calculated

Examples

library(adheRenceRX)
library(dplyr)

toy_claims %>% 
  group_by(ID) %>% 
  propagate_date(.date = date, .days_supply = days_supply) %>% 
  identify_gaps() %>% 
  calculate_pdc()
  
#OR, one could group by the ID and episode of care like...
toy_claims %>% 
  group_by(ID) %>% 
  propagate_date(.date = date, .days_supply = days_supply) %>% 
  identify_gaps() %>% 
  rank_episodes(.permissible_gap = 30) %>% 
  ungroup() %>% 
  group_by(ID, episode) %>% 
  calculate_pdc()

Restructuring Dates to Remove Overlap

Description

This is a function meant to be utilized within propagate_date() in order to adjust pharmaceutical claims dates to prevent overlapping in adherence calculations, per Canfield SL, Zuckerman A, Anguiano RH, Jolly JA, DeClercq J. Navigating the wild west of medication adherence reporting in specialty pharmacy. J Manag Care Spec Pharm. 2019;25(10):1073-77.

Usage

date_check(df)

Arguments

df

a claims data frame with a date of a medication claim and the corresponding days supply

Value

A new claims data frame with an appended column, adjusted_date

Ranking Episodes of Care

Description

This is a helper function to assist rank_episodes

Usage

episode_check(df)

Arguments

df

a data frame with "gap", "initial_rank", and "permi_gap" columns appended from identify_gaps()

Value

a data frame with an "episode" column appended, which ranks episodes of care in time

Identify Gaps in Therapy

Description

Compute gaps in a patient's therapy from the end of their prior fill to the beginning of the next. This function assumes that one has arranged the dates and grouped appropriately outside of the function. The length of any gap will be appended to the row after the gap has occurred.

Usage

identify_gaps(.data)

Arguments

.data

data frame

Value

A new claims tibble with an appended column, gap

Note

This function relies an adjusted_date column to identify gaps in therapy. So, if you don't want to use propagate_date() beforehand, you'll need to rename the date variable you wish to use to adjusted_date.

Examples

library(adheRenceRX)
library(dplyr)

toy_claims %>% 
  filter(ID == "D") %>% 
  propagate_date(.date_var = date, .days_supply_var = days_supply) %>% 
  identify_gaps()

Adjust Overlapping Fill Dates

Description

When assessing pharmaceutical adherence, one should adjust overlapping dates forward for a specified group (e.g. patient ids or medication classes) so that there is no overlap in days supply. For example, if a patient receives a 30 days supply on January 1st, and another 15 days later, the next fill date should be moved up 15 days. This function is modeled after recommendations from Canfield SL, Zuckerman A, Anguiano RH, Jolly JA, DeClercq J. Navigating the wild west of medication adherence reporting in specialty pharmacy. J Manag Care Spec Pharm. 2019;25(10):1073-77.

Usage

propagate_date(.data, .date_var = NULL, .days_supply_var = NULL)

Arguments

.data

Data to be piped into the function

.date_var

Date, column indicating the date of a given fill

.days_supply_var

Integer, column indicating the days supply of a given fill

Value

The initial claims data frame with an appended column, adjusted_date

Note

This function relies on anydate to parse the users date variable into a date class. So, for most columns passed to .date_var, this function will run without warning or error. For example, anydate(30) will return "1970-01-31" even though 30 is most likely a days supply. If strange results are produced, double check that the date variable being specified is indeed a fill date.

Examples

library(adheRenceRX)
library(dplyr)

toy_claims %>% 
  filter(ID == "D") %>% 
  propagate_date(.date_var = date, .days_supply_var = days_supply)

Rank Episodes of Care

Description

This function identifies and labels all episodes of care for a given patient in chronological order. A new episode begins after a specified gap in therapy has occurred. It is meant to be used after one has appropriately adjusted dates (propagate_date()) and identified gaps in therapy (identify_gaps()).

Usage

rank_episodes(.data, .permissible_gap = NULL, .initial_rank = 1)

Arguments

.data

Data frame with a "gap" column appended from identify_gaps()

.permissible_gap

Integer value suggesting the maximum gap allowed before labeling a new episode of care

.initial_rank

Integer value to identify what the indexing rank should be (defaults to 1).

Value

The initial claims data frame with an episode column appended, which ranks episodes of care in time

Note

This function assumes an adjusted_date column, which is produced by the propagate_date() function and a gap column, which is produced by identify_gaps(). If you would like to rank episodes of care using other dates and a separate column for gaps, you'll need to rename those columns before passing the frame to rank_episodes(). Notably, this is on purpose as this step should almost always come after the former two.

Examples

library(adheRenceRX)
library(dplyr)

toy_claims %>% 
  filter(ID == "D") %>% 
  propagate_date() %>% 
  identify_gaps() %>% 
  rank_episodes(.permissible_gap = 20, .initial_rank = 1)

Summarise Gaps in Therapy

Description

This function serves as a convenience wrapper of dplyr::summarise(), which takes the grouped variables and summarises their gaps in therapy. This function is to be used after propagate_date().

Usage

summarise_gaps(.data)

Arguments

.data

Data to be piped into the function

Value

A summary of gaps in therapy

Note

This function relies an adjusted_date column to identify gaps in therapy. So, if you don't want to use propagate_date() beforehand, you'll need to rename the date variable you wish to use to adjusted_date.

Examples

library(adheRenceRX)
library(dplyr)

toy_claims %>% 
  filter(ID == "D") %>% 
  propagate_date(.date_var = date, .days_supply_var = days_supply) %>% 
  summarise_gaps()

A Toy Patient Medication Claims tibble

Description

This mock patient claims tibble is meant to test adheRenceRX with scenarios presented in Figure 1. of Canfield SL, Zuckerman A, Anguiano RH, Jolly JA, DeClercq J. Navigating the wild west of medication adherence reporting in specialty pharmacy. J Manag Care Spec Pharm. 2019;25(10):1073-77

Usage

toy_claims

Format

A tibble with 22 rows and 3 variables:

ID

patient ID to be grouped by

date

date of claim

days_supply

number of days supplied