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From |
Peter Rymkiewicz <peter.rymkiewicz@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Fri, 06 Dec 2013 11:34:07 -0700 |

Thanks Adam, This is very useful. I appreciate your help. Peter Peter On 13-12-06 11:08 AM, Adam Olszewski wrote:

To answer your other question through the list: The estat phtest is basically a statistical test for the zero slope of residuals graph. As you can imagine, even a very very tiny slope will be "statistically significant" once the dataset is large enough. You can try to plot the residuals with a confidence interval band (twoway lfitci) and see for yourself, and perhaps this is the way to go if you want to just arbitrarily decide what is proportional and what is not. But if you see a noticeable slope, then of course the hazard is not proportional. You have to realize that the PH assumption is a mathematical approximation of the world that is not true in a "real" sense anyway. It actually just cannot be: if you observe your population for survival long enough, everyone will die and what is the hazard ratio at that time? The question is - does it matter for the purpose of what you are studying? 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> -- View this message in context: http://statalist.1588530.n2.nabble.com/Survival-Analysis-estat-phtest-with-very-large-sample-size-need-help-tp7580460.html Sent from the Statalist mailing list archive at Nabble.com. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Survival Analysis estat phtest with very large sample size--need help***From:*prymkiewicz <peter.rymkiewicz@gmail.com>

**Re: st: Survival Analysis estat phtest with very large sample size--need help***From:*Adam Olszewski <adam.olszewski@gmail.com>

**Re: st: Survival Analysis estat phtest with very large sample size--need help***From:*Peter Rymkiewicz <peter.rymkiewicz@gmail.com>

**Re: st: Survival Analysis estat phtest with very large sample size--need help***From:*Adam Olszewski <adam.olszewski@gmail.com>

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