Uppsala Reports Long Reads – Unmasking data in the COVID-19 vaccine era

Drug Safety Matters

Most pharmacovigilance professionals will have heard of masking – a statistical issue where reports for one drug hide signals for other drugs. But the problem gained fresh attention when record amounts of reports began piling up for the COVID-19 vaccines. How should we be unmasking data in the COVID-19 vaccine era?
This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC’s pharmacovigilance magazine, brought to you in audio format. Find the original article here.
After the read, we speak to data scientist Sara Vidlin, who authored the article, to learn more about masking and how to deal with it.
Tune in to find out:

• How masking evolves with the data
• Which methods can be used to unmask data
• What other pitfalls to watch out for when performing quantitative analyses

Want to know more?
In the very beginning of the vaccine rollout, the USA FDA observed how early signals for COVID-19 vaccines were delayed because of other drugs masking them, highlighting how masking is not a static phenomenon.
In 2013, Uppsala Monitoring Centre developed a simple strategy to uncover masking by identifying and removing influential outliers.

Join the conversation on social media
Follow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.
Got a story to share?
We’re always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!
About UMC
Read more about Uppsala Monitoring Centre and how we work to advance medicines safety.

章

1. Uppsala Reports Long Reads – Unmasking data in the COVID-19 vaccine era (00:00:00)

2. Intro (00:00:08)

3. Article read (00:01:25)

4. Welcome, Sara! (00:07:46)

5. Causes of masking (00:07:59)

6. Masking by COVID-19 vaccine reports (00:08:53)

7. Masking by other vaccine reports (00:12:13)

8. Solutions (00:13:40)

9. Raising awareness (00:14:48)

10. Other pitfalls of quantitative analyses (00:16:00)

11. Value of quantitative analyses (00:19:22)

12. Outro (00:20:14)