big_data_toy

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

datasets

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>.

Brennan Beal

adheRenceRX

Assess Medication Adherence from Pharmaceutical Claims Data

big_data_toy function

A <code>tibble</code> with 100,000 rows and 6 variables:<dl class="dl-horizontal">
 <dt>ID</dt><dd>patient ID to be grouped by</dd>
 <dt>date</dt><dd>date of claim</dd>
 <dt>days_supply</dt><dd>number of days supplied</dd>
</dl>

Format

A Toy Claims <code>tibble</code> for Performance Demonstration — big_data_toy

A <code>tibble</code> with 100,000 rows and 6 variables:<dl class='dl-horizontal'>
 <dt>ID</dt><dd>patient ID to be grouped by</dd>
 <dt>date</dt><dd>date of claim</dd>
 <dt>days_supply</dt><dd>number of days supplied</dd>
</dl>

big_data_toy: A Toy Claims `tibble` for Performance Demonstration

Description

Usage

Arguments

Format