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Re: st: Survival Analysis estat phtest with very large sample size--need help
From
Adam Olszewski <[email protected]>
To
[email protected]
Subject
Re: st: Survival Analysis estat phtest with very large sample size--need help
Date
Thu, 5 Dec 2013 21:41:22 -0500
Hi Peter,
As with any statistical test that uses a null hypothesis, the p-value
for the phtest is dependent on the sample size. These tests were not
developed for such large datasets. In population-based survival
analyses violations of PH assumptions are universal, just as linearity
assumptions are. One way to deal with it on a practical level is to
see how much inclusion of a time-varying effect would affect your main
effect HR. If the difference is null, then the PH violation is
practically of no significance. This helps if you are only interested
in one coefficient (more of a problem if your variable of interest
violates the PH, in which you should rethink your interpretation).
Other ways of dealing with it are: 1) relying on the graphical
interpretation of residuals alone, as you did, 2) using AIC as a
measure of model fit: does the inclusion of time-varying effects (or
stratification in Cox model) significantly alter model fit? More often
than no the AIC will actually increase.
I cannot quote you literature on this off the top of my head though.
Best,
AO
On Thu, Dec 5, 2013 at 9:26 PM, prymkiewicz <[email protected]> wrote:
> Hi,
>
> I need a bit of help.
>
> I am doing a survival analysis on a large population ~435732 people. I have
> been testing the PH assumption using estat phtest and schoenfled residuals.
> I believe that the large sample size is causing the phtest indicate evidence
> against the PH assumption while the schoenfeld plot would indicate that the
> model variables adheres to the PH assumption. I have included the global and
> individual variable tests, as well as the plot for one of our variables
> (mets). Could you let me know the cause of this and could you let me know if
> there are alternative methods or references in literature acknowledging the
> phtest and alternative methods for very large study populations.
>
> Thanks,
> Peter
>
> . estat phtest, detail
>
> Test of proportional-hazards assumption
>
> Time: Time
> ----------------------------------------------------------------
> | rho chi2 df Prob>chi2
> ------------+---------------------------------------------------
> male | 0.01247 11.18 1 0.0008
> age | 0.05331 252.06 1 0.0000
> 0b.urban | . . 1 .
> 1.urban | -0.01640 19.44 1 0.0000
> 99.urban | -0.00225 0.36 1 0.5472
> 1b.quintile | . . 1 .
> 2.quintile | -0.00620 2.74 1 0.0976
> 3.quintile | -0.00745 3.98 1 0.0461
> 4.quintile | -0.00025 0.00 1 0.9457
> 5.quintile | -0.00311 0.70 1 0.4039
> 99.quintile | -0.00119 0.10 1 0.7499
> mi_1 | -0.00795 4.63 1 0.0314
> chf_1 | -0.02492 47.01 1 0.0000
> pvd_1 | 0.00206 0.32 1 0.5734
> cevd_1 | -0.02076 32.80 1 0.0000
> dem_1 | 0.00630 3.05 1 0.0807
> copd_1 | -0.01545 17.10 1 0.0000
> rheum_1 | -0.00840 5.06 1 0.0245
> pub_1 | -0.00712 3.67 1 0.0553
> mildld_1 | -0.00959 6.59 1 0.0102
> diab_uc_1 | -0.02159 34.32 1 0.0000
> diab_c_1 | 0.01731 21.99 1 0.0000
> para_1 | -0.01532 17.14 1 0.0000
> rd_1 | -0.03307 82.48 1 0.0000
> cancer_1 | -0.03237 72.39 1 0.0000
> mlsd_1 | -0.00246 0.43 1 0.5099
> mets_1 | -0.06236 272.50 1 0.0000
> hiv_1 | -0.00863 5.31 1 0.0213
> ------------+---------------------------------------------------
> global test | 1392.71 26 0.0000
> ----------------------------------------------------------------
>
> <http://statalist.1588530.n2.nabble.com/file/n7580460/phtest_plot_mets_1.jpg>
>
>
>
> --
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