Novel algorithms for calculating proportion of days covered (PDC) in real-world datasets
The challenge:
To improve estimations of treatment adherence based on proportion of days covered (PDC) in real-world drug-dispensing dataset where all possible medication distribution points cannot be controlled. The study was conceived and funded by one of the UK’s largest online pharmacy chains to better understand their customers.
What we did:
- Reviewed the literature to identify existing algorithms to calculate PDC, including the range of variable definitions of the numerator and the denominator that has been used historically.
- Proposed several approaches to defining the denominator for the calculation of PDC, under varied assumptions, in order to solve the problem of accurately estimating adherence to daily, long-term medications.
- Presented and explained the proposed algorithms using hypothetical examples, followed by applying and evaluating in real-world data examples from UK online pharmacy data.
The outcome:
We produced three new algorithms for calculating PDC in real-world datasets and outlined how the different PDC algorithms may be applied depending on the research question at hand. Adherence estimate accuracy could be improved if legitimate gaps between medication refills are considered in the algorithm. The research output is currently under review for a peer-reviewed publication and for a scientific conference. Once published, the algorithms will be freely available for researchers to use globally.
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ACE inhibitors, statins and thyroid hormones
