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If you were taught to test for proportional hazards, talk to your teacher.
The proportional hazards assumption is implausible in most #randomized and #observational studies because the hazard ratios aren't expected to be constant during the follow-up. So "testing" is futile.
But there is more 👇
If you were taught to test for proportional hazards, talk to your teacher.
The proportional hazards assumption is implausible in most #randomized and #observational studies because the hazard ratios aren't expected to be constant during the follow-up. So "testing" is futile.
But there is more 👇
Comments
https://doi.org/10.1093/aje/kwae361
Easy-to-implement survival analysis methods that don't rely on proportional hazards are typically preferred.
The argument in 3 steps 👇
EPIDEMIOLOGY 2010
https://journals.lww.com/epidem/fulltext/2010/01000/the_hazards_of_hazard_ratios.4.aspx
Hazard ratios have a built-in selection bias because of depletion of susceptibles. Also, reporting only hazard ratios is insufficient because we also need (adjusted) absolute risks for sound decision making.
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@jama.com 2020
https://jamanetwork.com/journals/jama/article-abstract/2763185
Several examples show that hazards aren't expected to be proportional because either the effect isn't constant or the selection bias isn't constant.
An exception: null effect of treatment (hazard ratio=1)
...
@amjepi.bsky.social 2025
https://doi.org/10.1093/aje/kwae361
The proportional hazards assumption is generally superfluous. We encourage the use of survival analysis methods that produce absolute risks and that don't require constant hazard ratios.