Everyone Uses Censoring Assumptions… But They Fail When Leaving the Study Is Part of the Outcome

Censoring is one of the most common assumptions in epidemiology and survival analysis. It is often treated as a routine technical step for handling people who leave observation before the study ends. But what if leaving the study is not random noise—and is actually part of the outcome process itself? In this episode, we break down why censoring assumptions often fail, how loss to follow-up can distort longitudinal research, and why disappearing from the dataset is not the same thing as disappearing from risk.👉 Enjoyed the episode? Follow the show to get new episodes automatically.If you found the content helpful, consider leaving a rating or review—it helps support the podcast.For business and sponsorship inquiries, email us at:📧 [email protected]: https://www.youtube.com/@BJANALYTICSInstagram: https://www.instagram.com/bjanalyticsconsulting/Twitter/X: https://x.com/BJANALYTICSThreads: https://www.threads.com/@bjanalyticsconsulting