Timeline for Can I retain the ordinal nature of a predictor while answering a question about it that is inherently binary?
Current License: CC BY-SA 4.0
7 events
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| Aug 28 at 13:13 | vote | accept | mkt | ||
| Aug 27 at 11:45 | comment | added | Frank Harrell | Think about a very simple model containing a continuous predictor such as blood pressure. How to you make a seemingly binary decision to start antihypertensive medication based on blood pressure? You estimate the risk of a bad outcome at the observed bp, with and without receiving the drug. There is no dichotomization of bp at any point. | |
| Aug 27 at 10:11 | comment | added | mkt | Thank you. I'd appreciate it if you could also address the part of the question about how to use the fitted ordinal model to take a binary decision (intervene or not). The intervention would apply to a subset of the predictor levels. If that works better as a separate question with more elaboration, I can create a new one. | |
| Aug 27 at 10:02 | comment | added | Martin Modrák |
I would argue that the brms approach (while IMHO great) isn't the only option that respects the ordinal nature of X (e.g. monotonic splines are likely a decent option as well). Tried to make that into another answer.
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| Aug 26 at 22:07 | comment | added | Frank Harrell |
If you used a cubic polynomial you will fit all the points so that's effectively categorical. Yes re: brm.
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| Aug 26 at 15:36 | comment | added | mkt |
Thank you, Frank. I was not aware that using polynomial contrasts with lm() had problems - can you point me to some more information about why this doesn't work? I'm happy to use brms, though. I assume something like brm (Y~ mo(X)))?
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| Aug 26 at 15:18 | history | answered | Frank Harrell | CC BY-SA 4.0 |