Timeline for Checking cox.zph in R after time transformation of covariates
Current License: CC BY-SA 3.0
7 events
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| Dec 12, 2013 at 16:15 | comment | added | Frank Harrell | Not clear on how that makes for any better satisfaction of model assumptions. See my first note regarding how many variables seem to operate in PH and how that figures into the choice of another model such as an accelerated failure time model with hazard ratios converging to 1.0. | |
| Dec 12, 2013 at 13:33 | comment | added | Nicola Dinapoli | Thank you very much Prof. Harrell (great job with your book RMS that I own since one year ;-) ). Now I'm moving to use a Cox-Aelen model, and I'll try to validate it by using c-index and calibration procedure to detect model performance. | |
| Dec 11, 2013 at 22:16 | comment | added | Frank Harrell |
I understand that but you can't compute functions of time and call that a proper time-dependent covariate in a Cox model. Time-dep. covariates change the likelihood function. You are still effectively using static covariates since you are not following the instructions for proper use of coxph with the start, stop notation. And dividing a covariate by absolute time is a strange way to incorporate non-constant covariate effects.
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| Dec 11, 2013 at 16:13 | comment | added | Nicola Dinapoli | I didn't understand your first answer. I meant that the the assumption of constant effect over time of covariates is not satisfied by the Cox model in our case. What I'd like to do is to define a model that can describe this kind of behavior for covariates I analyzed. The exposition to the factors (except for Age, Sex) is given at the beginning of the observation time. Some covariates (as tumor grade in our dataset) seem to affect the outcome (overall survival) above all in the first 2-3 years of follow up, and it is consistent with other examples in oncological literature. | |
| Dec 10, 2013 at 12:41 | comment | added | Frank Harrell |
You did not read my note, which described the two reasons why the above strategy is not appropriate. Note that to have time-dependent covariates in coxph you need to provide "start time, stop time" to Surv and to have multiple records per subject.
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| Dec 9, 2013 at 15:05 | comment | added | Nicola Dinapoli |
I created a model with a function for creating time dependent covariates using the following code: coxph(formula = Surv(OS, OSCheck) ~ Age + Sex + RTCT + PathologicalTstage + Residualtumor + RTunexpectedinterruptiondaysduetomedicalreasonspostoper + tt(PathologicalNstage) + tt(Gradeofhistology) + tt(ChemotherapyCyclesadjuvant), data = dataset.IMP.1, tt = function(x, t, ...) (x/t)) I used a transformation of covariates for the inverse of time. I'd like to check, using something like cox.zph if this assumption is correct.
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| Dec 9, 2013 at 13:32 | history | answered | Frank Harrell | CC BY-SA 3.0 |