Drug adherence and persistence are commonly assessed using longitudinal claims and electronic health record databases, but the resulting measures are not direct observations of medicine-taking. In this QCast episode, co-hosts Jullia and Tom examine what these data sources can show, how adherence differs from persistence, and why analytical definitions influence the interpretation of treatment exposure over time.
The practical challenges surrounding drug adherence and persistence often sit in details such as overlapping prescriptions, dose changes, treatment switches and permissible gaps. Thresholds can simplify reporting while masking small differences between patients, and complex medication histories may make single-drug measures difficult to interpret. Clear definitions and transparent assumptions help clinical development and real-world evidence teams understand what has been measured and what remains uncertain.
Adherence describes how closely medicine use follows the agreed regimen during a defined period. It can include whether treatment was initiated and how consistently the regimen was implemented. Persistence addresses a different question: how long treatment continued before the patient met the study definition of discontinuation.
Medication possession ratio and proportion of days covered estimate whether medicine was available, but their results depend on how the analysis handles days’ supply, early refills and overlapping prescriptions. Persistence estimates also require a defined grace period, together with rules for restarts, switching and loss to follow-up. Different decisions can produce different results from the same underlying records.
A recorded treatment gap does not necessarily indicate poor adherence. It may reflect a planned dose change, an adverse event, a switch to another therapy or a clinical decision to stop treatment. Polypharmacy and multimorbidity add further complexity because a patient may follow one regimen closely while using another differently.
Jullia
Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.
Tom
I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.
Jullia
Whether you’re in biotech, pharma or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.
Tom
Today we’re talking about drug or medication adherence, which is often treated as a question of patient behaviour. Now when we’re working with longitudinal claims or electronic health record data, is it equally a measurement problem?
Jullia
Yes, so you see, these databases rarely show us someone taking a medicine. Rather they show events around medicine use, such as a prescription being written, a product being dispensed or a claim being reimbursed. Researchers then use those events to estimate exposure, so the definitions and processing rules can materially affect the result.
Tom
Before we get into those rules, how should we distinguish adherence from persistence? The terms are often used as though they mean the same thing.
Jullia
Adherence describes how closely medicine use follows the agreed regimen during a defined period. Persistence describes how long treatment continues before it’s considered to have stopped. Someone can remain on treatment for a year but refill late or miss doses, so they’re persistent but may have poor adherence. Another person may follow the regimen closely for three months and then discontinue, which gives us a different pattern.
Tom
And adherence itself can refer to different points in the treatment journey, can’t it?
Jullia
Yes. Initiation asks whether the patient starts the medicine after it’s prescribed. But implementation concerns how closely use follows the regimen while treatment continues, and discontinuation marks when it stops. We can also distinguish primary adherence, whether the first prescription is obtained, from secondary adherence, which looks at refill behaviour after treatment has begun.
Tom
So if a dataset starts with dispensing, patients who never collect that first prescription could be invisible?
Jullia
Exactly. The study might describe refill behaviour accurately among people who started treatment, while saying nothing about those who received a prescription but never filled it. That’s why the available data needs to match the research question.
Tom
Now claims and EHR databases provide different views of that journey. What does each contribute?
Jullia
So, claims data often provides large-scale records of reimbursed or dispensed medicines. They can show quantities supplied, refill intervals, treatment gaps and apparent switches over long observation periods. However, a completed claim only tells us that medicine was available to the patient. It doesn’t confirm ingestion, timing or correct administration.
Tom
A common assumption is that EHR data solves that limitation because they contain more clinical detail. Is that too generous?
Jullia
I’d say it is. See, EHRs may add diagnoses, encounters, patient characteristics, recorded outcomes and treatment changes, which can help interpret a pattern. But a prescribing record reflects what was intended, not necessarily what was dispensed or taken. Free-text instructions, overlapping prescriptions and incomplete discontinuation records can all complicate the estimate.
Tom
Could you give an example of where that clinical context changes the interpretation?
Jullia
Yes, so suppose the data shows a gap after a patient’s dose was changed following an adverse event. Claims alone might make that look like poor persistence. An EHR could show that the original prescription was deliberately stopped and replaced with a different strength or medicine. The gap hasn’t disappeared, but its meaning has changed.
Where linkage is available and suitable, the combined data may provide a more complete view. A prescribing record may identify when therapy was ordered, while dispensing data show whether it was obtained. Clinical records may then help explain a switch or planned interruption.
Tom
Now once the cohort and data source are chosen, analysts often turn to medication possession ratio or proportion of days covered. How do those measures differ?
Jullia
So, medication possession ratio, or MPR, generally compares the total amount of medicine supplied with the length of the observation period. Depending on the definition, it may exceed 100% when a patient refills early and accumulates supply, although some analyses cap it. Proportion of days covered, or PDC, estimates the percentage of days on which medicine is considered available and usually deals more explicitly with overlapping supplies.
Tom
Here PDC can sound like the more precise answer. Does it remove the judgement from the analysis?
Jullia
Not necessarily no, because both measures depend on choices. Analysts still need to define follow-up, derive days’ supply and decide whether an early refill carries forward. They also need rules for inpatient stays, dose changes, switches and overlapping prescriptions. Two well-conducted analyses of the same records can produce different estimates if those decisions differ.
Tom
Let’s stay with the dose-change example for a minute. What could happen if the database records a new prescription, but the old supply hasn’t technically run out?
Jullia
Well, one approach might assume the patient finishes the old supply before starting the new dose. While another might treat the new prescription as replacing it immediately. If the directions are incomplete or held in free text, the analyst may have to make a further assumption. Each approach changes the days considered covered, so it belongs in the methods rather than being hidden in data processing.
Persistence requires its own treatment-gap definition. Researchers usually identify a treatment start and follow the patient until a gap exceeds a defined grace period. But the allowable gap needs a rationale. A 30-day rule and a 60-day rule can classify the same patient differently, particularly when refill timing varies.
Tom
What about someone who restarts after that gap?
Jullia
So, the analysis has to decide whether that’s a new treatment episode, a continuation after interruption or still a discontinuation for the primary endpoint. Switching also needs clear handling. Patients who leave the database or reach the end of available follow-up may need to be censored rather than treated as if they stopped therapy.
Thresholds can make the output look simpler by turning an adherence estimate into ‘adherent’ or ‘non-adherent’. But information can be lost when a continuous measure is converted into a category. An 80% cut-off is commonly used, but its clinical meaning can vary by medicine, condition and outcome. Patients with 79% and 81% coverage may therefore be placed in different groups, even though their observed medication availability is very similar.
Tom
Would you avoid thresholds altogether?
Jullia
Not necessarily. They can support a clear analysis when the cut-off is clinically justified and specified in advance. It can also be useful to report the continuous distribution or run sensitivity analyses around the chosen rule. Really, the problem arises when a conventional threshold is treated as universally meaningful.
Different observation windows, permissible gaps or approaches to early refills can shift the reported results. Even the way a dose change is handled can affect comparability. Readers need enough detail to understand what was measured before they compare the numbers.
These methods often assume one medicine, one condition and a stable regimen, but many real-world patients have more complex treatment histories. A patient may take several medicines for multiple chronic conditions, receive prescriptions from different clinicians and change regimens over time. They may adhere closely to one treatment but not another. A single overall label can flatten those differences.
Tom
Isn’t there also a risk of interpreting every stop or gap as a patient failing to follow treatment?
Jullia
Yes, and that can be misleading. A medicine might be stopped after an adverse event, a clinical review or a change in diagnosis. Treatment burden and regimen complexity may also affect use, while healthcare access can influence refill timing. Database records can reveal the pattern, but they may not contain the patient’s reason for it.
Tom
So, how should a team handle that uncertainty without making the analysis so cautious that it becomes unhelpful?
Jullia
I’d start by being precise about the estimand or research question. Which treatment, which patients, which period and which aspect of use are we assessing? Then define treatment episodes and rules that fit that question. Sensitivity analyses can show whether findings change under plausible gap, censoring or overlap assumptions. Interpretation should stay within what the data can support.
Tom
For listeners planning one of these studies, what are the takeaways you’d want them to carry into the protocol and analysis plan?
Jullia
First, separate adherence from persistence because they answer different questions. Second, treat prescriptions and dispensings as proxies for medicine use, with different strengths and limitations. Finally, document the choices that construct the measure, including follow-up, days’ supply, permissible gaps, switching, dose changes and censoring.
Really, the headline estimate is the endpoint of a chain of design and data-processing decisions.
With that, we’ve come to the end of today’s episode on drug adherence and persistence in longitudinal claims and EHR databases. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode and share it with a colleague. And if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.
Tom
Thanks for tuning in, and we’ll see you in the next episode.
QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.
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