Re: st: RE: RE: RE: Competing risk calulations based on stratified Cox model

[Adam Olszewski <adam.olszewski@gmail.com>]

Hi,
I am actually quite interested in the subject.  Can you indicate any
literature that dealt with the subject of "significance" of PH
violation in very large data sets and for alternative assessments of
the assumption?
Best,
AO

On Wed, Sep 18, 2013 at 3:36 PM, Kieran McCaul <kieran.mccaul@uwa.edu.au> wrote:
> ...
>
> Hi Christel,
>
> Before considering how to get the cumulative incidence curves, can you tell me how you've determined that you have a proportionality problem?
>
> Are you relying principally on p-values when testing the proportional hazards assumption?
>
> If you have 300,000 subjects then presumably you have many events and hence sufficient power to declare small effects "statistically significant".
>
> In such a setting, it's quite possible to find covariates that fail the proportional hazards test "statistically", but for which the variation in proportionality is small and could be ignored.  In essence, the proportionality test is looking at a regression line and testing the null that the slope is zero: with a large sample, a slope that is very near zero could be " statistically significant".
>
> In the output from the proportionality test you need to look at the rho values.  The p-values are testing rho=0.
> Small rho values don't indicate a problem with proportionality, regardless of the size of the p-value.
>
> By small, I mean something like |rho| <0.02 or <0.03, depending on my mood.  I tend to rely more on graphical assessments.
>
> Use the -estat phtest- options to produce plots of the scaled Schoenfeld residuals against time overlayed with a lowess smoother: if the smoother line looks flat, there's little evidence of a problem with proportionality.
>
>
> Kieran
>
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Christel Häggström
> Sent: Wednesday, 18 September 2013 9:04 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: st: RE: RE: Competing risk calulations based on stratified Cox model
>
> Dear Kieran,
>
> Thank you for the help and quick reply.
>
> I can fit a combined Cox model as in those pages you referred to, if I have understand the documentation right, in my data it looks something similar to this:
> xi:stcox cause#(BMI_categories ), nolog strata(cause birth_cohort_categories smoking_categories sub_cohort_categories )
>
> There are different effect of the covariates I need to stratify for both of the endpoints, and none of these covariates satisfy the proportional hazard assumption. The main cause are a disease among the elderly, competing cause are all-cause death and my dataset contain approx. 300 000 subjects.
>
> What I want to do is to calculate the cumulative incidence based on this Cox model, and plot that cumulative incidence over the timescale, but I don't know how I should write the stcompadj function to do this possible. Suggestions?
>
> Best regards,
> Christel
>
>
>
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Kieran McCaul
> Sent: den 18 september 2013 10:37
> To: statalist@hsphsun2.harvard.edu
> Subject: st: RE: Competing risk calulations based on stratified Cox model
>
> ...
>
> -stcompadj- is a user-written program.  There are ancillary files that can be downloaded as well and one of these is a PDF file that outlines the general approach used.
>
> If you look at Pages 6 and 7 of this PDF file, you can see how the data is set up for analysis using standard cox models.
> Why not try doing this and test proportionality in each of the models?  Then try the combined model (bottom of page 7) with the stratum variable needed for the competing risk analysis plus the variables that you need to stratify on.
>
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Christel Häggström
> Sent: Wednesday, 18 September 2013 4:08 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Competing risk calulations based on stratified Cox model
>
> Dear statausers,
>
> I am going to perform a competing risk analysis, and plan to use stcompadj for the ability to adjust for covariates. I have previously calculated hazard ratios with the same material with stcox and there are some covariates not satisfying the proportional hazard assumption which has been stratified for within the Cox model (strata option).
>
> I planned to use the same model for cumulative incidence calculations, but have not found an option within stcompadj how I can handle the covariates not satisfying proportional hazards assumption.  Any suggestions?  Or any alternative commands to use?
>
> Thank you for the consideration.
> Christel
>
>
>
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