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Re: st: Survival Analysis estat phtest with very large sample size--need help


From   Adam Olszewski <adam.olszewski@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Survival Analysis estat phtest with very large sample size--need help
Date   Fri, 6 Dec 2013 12:59:39 -0500

This may not be feasible for your thesis purpose, but from the
practical point of view, -stcox- with -tvc- option on a large dataset
is extremely computationally intense and may just be unacceptable (and
multi-core Stata does not improve on that). If you want to "screen"
coefficients with/without tvc. then using a flexible parametric model
(user-written -stpm2- command) is just unbelievably faster.
A reference for that would be:
Royston P, Parmar MK. Flexible parametric proportional-hazards and
proportional-odds models for censored survival data, with application
to prognostic modelling and estimation of treatment effects. Stat Med.
2002;21(15):2175-97.
AO

On Fri, Dec 6, 2013 at 12:55 PM, Peter Rymkiewicz
<peter.rymkiewicz@gmail.com> wrote:
> Hi Adam,
>
> Thanks for your response. I agree with all that you have said... part of the
> issue is that this work is towards a thesis and I need to reference
> literature or statistics materials. Will try to use the TVC option with the
> original model and look to see what the resulting effect is on the HR of
> each of the covariates in  the model.
>
> I am also running an alternative model which actually captures time
> dependance. as you predicted the AIC is higher but this model actually
> includes information past baseline.
>
> Thanks,
> Peter
>
> On 13-12-05 7:41 PM, Adam Olszewski wrote:
>>
>> 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 <peter.rymkiewicz@gmail.com>
>> 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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