Data Science x Public Health
This podcast discusses the concepts of data science and public health, and then delves into their intersection, exploring the connection between the two fields in greater detail.
Data Science x Public Health
Everyone Uses Censoring Assumptions… But They Fail When Leaving the Study Is Part of the Outcome
Use Left/Right to seek, Home/End to jump to start or end. Hold shift to jump forward or backward.
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:
📧 contact@bjanalytics.com
Youtube: https://www.youtube.com/@BJANALYTICS
Instagram: https://www.instagram.com/bjanalyticsconsulting/
Twitter/X: https://x.com/BJANALYTICS
Imagine watching a movie where a character just, you know, casually walks off screen. You naturally assume they went to grab a coffee or something. You don't assume they fell through a hidden trapdoor.
SPEAKER_00Yeah, that would be ridiculous.
SPEAKER_01Welcome to this deep dive. Today we're looking at a presentation titled They Didn't Just Disappear. And our mission for you is to uncover why missing data in health studies isn't just like a mathematical nuisance.
SPEAKER_00No, it's actually a massive hidden blind spot that is completely distorting our understanding of medicine.
SPEAKER_01Okay, let's unpack this. In epidemiology, there's this baseline concept called censoring, which, I mean, it sounds totally harmless.
SPEAKER_00It sounds purely administrative. Like when someone leaves a clinical trial because um maybe they move away or they switch healthcare systems or just lose their coverage.
SPEAKER_01The analysts just censor them from the data.
SPEAKER_00Aaron Powell Exactly. They just click the timeline and move on. Basically, treating that exit as random background noise.
SPEAKER_01Which goes back to the movie thing. We assume they went for that coffee. We assume their reason for leaving the study has absolutely nothing to do with the actual drug or treatment being evaluated.
SPEAKER_00Aaron Powell What's fascinating here is the reality of what researchers call informative exit. Because dropouts are rarely just neutral interruptions.
SPEAKER_01They're falling through the trapdoor.
SPEAKER_00They really are. I mean, a patient doesn't typically just, you know, lose follow-up. They often stop showing up because, say, the treatment side effects became completely intolerable.
SPEAKER_01Oh, or they lose their health insurance specifically because their illness got so severe they couldn't maintain their job.
SPEAKER_00They didn't vanish randomly. Illness itself caused the disappearance.
SPEAKER_01Aaron Powell So what does this all mean? The statistical models still run, right? Like the Kaplan-Meyer curves still plot on the screen.
SPEAKER_00Oh, yeah. The software will happily calculate hazard ratios all day long. It'll print out those survival curves, like everything is perfectly fine.
SPEAKER_01But it's not fine.
SPEAKER_00No, it creates a really dangerous illusion of rigor. Because if an analysis treats dropping out as random chance, the math systematically deletes the highest risk, most vulnerable people from the equation.
SPEAKER_01We end up giving a new medication an A plus simply because everyone who failed the treatment fell through the trapdoor and just, well, isn't being graded anymore.
SPEAKER_00And this distortion is especially dangerous in specific fields. We see a lot in HIV care cascades, um, maternal health follow-ups, and substance use treatment programs.
SPEAKER_01Oh wow. Yeah, that makes complete sense. In those areas, leaving the data set isn't just a bookkeeping error.
SPEAKER_00Aaron Powell No, not at all. It is often a screaming siren about worsening risk or a total institutional failure.
SPEAKER_01Aaron Powell Like if a patient in a substance use trial just stops answering calls, they probably didn't just get busy, they likely relapsed.
SPEAKER_00Which is the exact outcome the study is supposed to be measuring in the first place.
SPEAKER_01Aaron Powell Here's where it gets really interesting. How do we actually fix this? Because we can't just ignore the missing data. Good epidemiology has to treat dropping out as a causal question, not just an administrative step.
SPEAKER_00Right. You have to ask what leaving actually means. And researchers use specific mathematical tools to address this, like uh inverse probability weighting or joint modeling.
SPEAKER_01Aaron Powell or alternative estimates. Right, wait, let me see if I have the weighting thing straight.
SPEAKER_00Yeah, go for it.
SPEAKER_01So instead of pretending those missing patients vanished, it basically works like a megaphone. Say a patient experiencing housing instability drops out of the study. The model finds a similar patient who actually stayed in the study and amplifies their data. You give more statistical weight to the survivors who look just like the people who fell through the cracks.
SPEAKER_00That is spot on. You're adjusting the scales to reflect reality. You design the study acknowledging that exiting the healthcare system might actually be a direct symptom of the illness itself.
SPEAKER_01And for you listening, this matters because the public health policies, the guidelines, and the actual treatments prescribed to you at the doctor's office are built entirely on this data. If the underlying math is blind to failure, your treatment plan might be too.
SPEAKER_00If we connect this to the bigger picture, the real lesson here is that disappearing from a data set does not mean disappearing from the actual risk process.
SPEAKER_01They're still out there.
SPEAKER_00Exactly. Someone dropping off a spreadsheet is still out there, potentially getting sicker.
SPEAKER_01Which leaves us with a pretty unsettling thought to end on. If our health systems consistently lose track of the most vulnerable and unstable patients, are we inadvertently building an entire foundation of medical knowledge that only truly applies to the highly privileged?