Considerations for pharmacoepidemiology study design

By krhendrickson in epidemiology biostatistics

July 26, 2023

Right now, an unorganized list of concepts I don’t want to forget about.

Pharmacoepidemiology, general

  • Rapid changes in the natural history of a disease or changes in treatment decisions will mean strong effects in relation to calendar time.

    • Example: The management of COVID-19 improved throughout the pandemic independent of medication use. If you wanted to assess the comparative effectiveness of a drug, you would need to control for calendar time.
  • Nonuser designs tend to suffer from confounding by indication, as patients receiving treatment will be different from those receiving no treatment.

  • Immortal time bias - this bias results when there is a period of follow-up time when it is impossible for the outcome to occur in the exposed individual.

    • The classic example is when person-time before an exposure is classified as “exposed”. By design that person had to have survived up to the exposure point, so this is not a real comparator to the unexposed person. This time should be categorized as “unexposed”.
    • Using a risk-set sampling design, where all people are eligible to be “nonusers” regardless of whether they later become “users”, avoids this bias.
  • New user study design

    • For drugs with intermittent use, lookback period will have a strong impact on classification.

Using Claims Data

  • Including open claims presents a challenge of defining a relevant denominator. Main concern is the under-inclusion of healthy individuals who are not"active" in the healthcare system.


Franklin, J.M., et al. Real-World Evidence for Assessing Pharmaceutical Treatments in the Context of COVID-19.

Posted on:
July 26, 2023
2 minute read, 249 words
epidemiology biostatistics
causal inference
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