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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
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
<[email protected]> 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 <[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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