Assess Medication Adherence from Pharmaceutical Claims Data.
adheRenceRX
Check out our site adheRenceRX
The goal of adheRenceRX is to provide a slightly opinionated set of functions to allow researchers to assess medication adherence in the most flexible way possible. The goal was (is) to write piping-friendly verbs the “tidy” way to allow users to manipulate their data as they’d like without storing data multiple times into their environment. In tidy fashion, we aimed to create functions that did only one thing, ideally that thing is obviated by the name of the function! So, the package makes assessing adherence as flexible as possible with some key things left in the hands of the researcher. The final value is that functions without vectorised solutions (propagate_date() and rank_episodes()) are written with C++ allowing speed and performance when you’d rather do research than run a function for an hour!
This was a lot of fun to build but is still in production. If you find errors, or know things you’d like to see done differently, reach out!
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("btbeal/adheRenceRX")
Overview
Much of the inspiration for this package came from conversations with analysts who struggle to deal with the non-intuitive ways to deal with medication adherence calculations from pharmaceutical claims data.
Our package is built around suggestions from Canfield and colleagues (2019) who note that overlapping fill dates should be pushed forward and never counted backwards, to assess adherence properly. For that reason, our package revolves around the first step of creating adjusted dates prior to any other calculation. Next, one can identify the gaps, rank episodes of care, and calculate pdc. The purpose of the package was to be as flexible as possible. So, there will be a lot left to be done by the researcher (on purpose!). For example, are there time periods you’re particularly concerned with? Patient filters? Other groupings (maybe episode of care?). Those are meant to be defined with dplyr verbs outside of our functions.
Our verbs to date are:
propagate_date()identify_gaps()orsummarise_gaps()rank_episodes()calculate_pdc()
For the most part, our verbs assume that dates have been propagated forward and gaps have been properly identified. This is on purpose but is subject to change in the future.
Examples
More examples of use can be found on within each functions documentation; however, this should provide a decent overview of how the package is to be used.
library(adheRenceRX)
library(dplyr)
# manipulate toy_claims, which has IDs based on the Canfield 2019 paper
toy_claims %>%
# filter for some interesting IDs
filter(ID %in% c("B", "D")) %>%
# Group by them (grouping not limited, of course)
group_by(ID) %>%
# propagate the dates forward within those groups
propagate_date(.date_var = date, .days_supply_var = days_supply)
#> # A tibble: 14 x 4
#> # Groups: ID [2]
#> ID date days_supply adjusted_date
#> <chr> <date> <dbl> <date>
#> 1 B 2020-01-01 30 2020-01-01
#> 2 B 2020-01-31 30 2020-01-31
#> 3 B 2020-03-01 30 2020-03-01
#> 4 B 2020-05-30 60 2020-05-30
#> 5 B 2020-06-29 60 2020-07-29
#> 6 B 2020-07-29 30 2020-09-27
#> 7 B 2020-08-28 30 2020-10-27
#> 8 B 2020-09-27 30 2020-11-26
#> 9 D 2020-01-01 60 2020-01-01
#> 10 D 2020-01-31 60 2020-03-01
#> 11 D 2020-03-01 60 2020-04-30
#> 12 D 2020-05-30 30 2020-06-29
#> 13 D 2020-08-28 60 2020-08-28
#> 14 D 2020-09-27 30 2020-10-27
Notice that several rows have been pushed forward to account for overlaps in date. Also notice that the output changes the date and days supply variable to date and days_supply while adding an adjusted_date variable. The adjusted_date variable is used by some of the other functions so it is important to complete this step first.
Once the dates have been adjusted, we can identify gaps in therapy with identify_gaps() or summarise them with summarise_gaps().
# The same code from above
toy_claims %>%
filter(ID %in% c("B", "D")) %>%
group_by(ID) %>%
propagate_date(.date_var = date, .days_supply_var = days_supply) %>%
# But now we can identify gaps
identify_gaps()
#> # A tibble: 14 x 5
#> # Groups: ID [2]
#> ID date days_supply adjusted_date gap
#> <chr> <date> <dbl> <date> <dbl>
#> 1 B 2020-01-01 30 2020-01-01 0
#> 2 B 2020-01-31 30 2020-01-31 0
#> 3 B 2020-03-01 30 2020-03-01 0
#> 4 B 2020-05-30 60 2020-05-30 60
#> 5 B 2020-06-29 60 2020-07-29 0
#> 6 B 2020-07-29 30 2020-09-27 0
#> 7 B 2020-08-28 30 2020-10-27 0
#> 8 B 2020-09-27 30 2020-11-26 0
#> 9 D 2020-01-01 60 2020-01-01 0
#> 10 D 2020-01-31 60 2020-03-01 0
#> 11 D 2020-03-01 60 2020-04-30 0
#> 12 D 2020-05-30 30 2020-06-29 0
#> 13 D 2020-08-28 60 2020-08-28 30
#> 14 D 2020-09-27 30 2020-10-27 0
# Or, we could just summarise them all:
toy_claims %>%
filter(ID %in% c("B", "D")) %>%
group_by(ID) %>%
propagate_date(.date_var = date, .days_supply_var = days_supply) %>%
# Summarising gaps
summarise_gaps()
#> # A tibble: 2 x 2
#> ID Summary_Of_Gaps
#> <chr> <dbl>
#> 1 B 60
#> 2 D 30
With the gaps identified, we can check for episodes of care using our rank_episodes() functions. Note that this function assumes that you’ve propagated your dates appropriately and identified all gaps. You can then tell our function what can be considered a permissible gap, and everything after a gap that large or more will be considered the next episode! Let me show you.
# The same code from above
toy_claims %>%
filter(ID %in% c("B", "D")) %>%
group_by(ID) %>%
propagate_date(.date_var = date, .days_supply_var = days_supply) %>%
# But now we can identify gaps
identify_gaps() %>%
# say that anything over a 10 day gap should count as the next episode
rank_episodes(.permissible_gap = 10)
#> # A tibble: 14 x 6
#> # Groups: ID [2]
#> ID date days_supply adjusted_date gap episode
#> <chr> <date> <dbl> <date> <dbl> <dbl>
#> 1 B 2020-01-01 30 2020-01-01 0 1
#> 2 B 2020-01-31 30 2020-01-31 0 1
#> 3 B 2020-03-01 30 2020-03-01 0 1
#> 4 B 2020-05-30 60 2020-05-30 60 2
#> 5 B 2020-06-29 60 2020-07-29 0 2
#> 6 B 2020-07-29 30 2020-09-27 0 2
#> 7 B 2020-08-28 30 2020-10-27 0 2
#> 8 B 2020-09-27 30 2020-11-26 0 2
#> 9 D 2020-01-01 60 2020-01-01 0 1
#> 10 D 2020-01-31 60 2020-03-01 0 1
#> 11 D 2020-03-01 60 2020-04-30 0 1
#> 12 D 2020-05-30 30 2020-06-29 0 1
#> 13 D 2020-08-28 60 2020-08-28 30 2
#> 14 D 2020-09-27 30 2020-10-27 0 2
Finally, an actual adherence calculation. This is fairly straightforward since the bulk of the work has been done adjusting your dates and then appropriately identifying the gaps in therapy. Still, more functions = more fun!
toy_claims %>%
group_by(ID) %>%
propagate_date(.date_var = date, .days_supply_var = days_supply) %>%
identify_gaps() %>%
calculate_pdc()
#> # A tibble: 3 x 4
#> ID total_gaps total_days adherence
#> <chr> <dbl> <dbl> <dbl>
#> 1 A 0 270 1
#> 2 B 60 330 0.818
#> 3 D 30 300 0.9
Enjoy!
That’s all we have for now. Again, this package is meant to provide some helper functions with the meat of the project coming from our propagate_date() and rank_episodes(). Notbaly, those tasks can’t be accomplished with dplyr alone (as they do not have vectorised solutions). For this reason, we’ve written some C++ functions to help you speed up the task!
Citations
- 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.