Timeline for answer to Adjusted Survival Curves Strata Specification with Continuous Predictor by Demetri Pananos
Current License: CC BY-SA 4.0
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| Sep 2, 2019 at 4:18 | comment | added | Demetri Pananos | @mindhabits ask that question in a separate thread. That way, people looking for the answer can easily find it. | |
| Sep 1, 2019 at 21:46 | vote | accept | mindhabits | ||
| Sep 1, 2019 at 20:28 | history | bounty awarded | mindhabits | ||
| Sep 1, 2019 at 17:11 | comment | added | mindhabits | Thanks for your input. This is getting away from the main question but I'd like your expertise on the matter. I am standardizing all my continuous covariates (and transforming them if needed), however I would like to express the change in the log hazard per 10 year change for my covariate "Age" as standard deviation does not make much sense for this. Just like any other regression model, this is okay to do as long as I specify this, correct? | |
| Sep 1, 2019 at 16:21 | comment | added | Demetri Pananos | Yes, that's the correct interpretation. | |
| Sep 1, 2019 at 16:00 | comment | added | mindhabits | There might be reason to transform the covariates given a positive skew to remove influence of extremes, correct? If I was to log10 transform, would the hazard ratios be interpreted as the change in the log hazard per standard deviation change in log10 predictor? | |
| Sep 1, 2019 at 15:40 | comment | added | Demetri Pananos |
The cox model is expresses the log hazard ratio as a linear combo of covariates. $\log(HR) = \beta_0 + \sum_i \beta_i x_i$. When you run summary on a coxph model, you will see the table returns coef and exp(coef). coef is the log hazard ratio (i.e. the $\beta$ in the model) and thus exp(coef) is the hazard ratio. No need to transform any covariates, you can leave them as they are.
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| Sep 1, 2019 at 4:11 | comment | added | mindhabits | Good points, I will try and pursue this. As a follow-up, why must the hazard ratio be interpreted as the change in the log hazard per standard deviation change. Is this true even though I have not taken the log of my covariates? What if I did transform my covariate? Let's say A, B, C are all continuous predictors. What is the interpretation if I standardize these covariates and take the log? Additionally, is it possible to just take the log of A and B and not C (but all three remain standardized)? | |
| Sep 1, 2019 at 2:47 | history | answered | Demetri Pananos | CC BY-SA 4.0 |