Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
| From | Yuval Arbel <yuval.arbel@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: stcox in case the ph-assumption is rejected |
| Date | Fri, 6 Jan 2012 16:24:48 +0200 |
I'm refering to my following working paper published at SSRN: http://ssrn.com/abstract=1973326 My intentions are only to supplement a footnote and present the HP-assumption tests in an appendix and not to go further beyond that. I provide these stratifications as an example. The question is whether in your opinion this is sufficient - or should I modify all the outcomes reported in the paper based on the -tvc- option.(On one hand, I would like to make here a careful econometric work. On the other hand, I consider whether it worth all the trouble or it might be sufficient to show that at least one stratification does not reject the PH-hypothesis) On Fri, Jan 6, 2012 at 3:46 PM, Maarten Buis <maartenlbuis@gmail.com> wrote: > You should use stratification only for those variables you do not care > about, as after stratification you can no longer include that variable > in your model, and thus not show what the effect of that variable is. > I would not use stratification for (pseudo-)continuous variables, > because it is an idea that is based on a small set of well defined > groups (or at least a number of well defined groups with a sufficient > number of observations in each group). Splitting a pseudo-continuous > variable at the mean sounds a bit too ad hoc for my taste to classify > as two well defined groups. > > Hope this helps, > Maarten (not Marteen) > > On Fri, Jan 6, 2012 at 2:23 PM, Yuval Arbel wrote: >> Thanks Marteen - that seems to be very helpful. >> >> I also thought about a different solution I would like to consult with >> you about: >> >> For each of the explanatory variables in the regression model I >> defined a dummy variable which receives 1 for periods whose numerical >> values are above or equal the sample mean and 0 otherwise. This >> provides several possible stratifications. I then ran the Cox >> regression on these dummy variables, where, as mentioned above, each >> of which provides a different stratification, followed by the >> PH-assumption test. Now and as we can see from the outcomes below - I >> can say that the outcomes of the Cox regression is valid only for >> stratifications where the PH-assumption is valid. >> >> Here is the output: >> >> . stcox mean_reduct_dum1 reductcurrent_mean_reduct_dum1 rent_net8_dum >> diff_stdmadadarea_dum diff_mortgage_dum perma >>> nentincomeestimate82_dum appreciation_dum,nohr >> >> failure _d: fail == 1 >> analysis time _t: time_index >> id: appt >> >> Iteration 0: log likelihood = -78368.249 >> Iteration 1: log likelihood = -75173.499 >> Iteration 2: log likelihood = -75117.414 >> Iteration 3: log likelihood = -75116.825 >> Iteration 4: log likelihood = -75116.825 >> Refining estimates: >> Iteration 0: log likelihood = -75116.825 >> >> Cox regression -- Breslow method for ties >> >> No. of subjects = 9547 Number of obs = 499393 >> No. of failures = 9547 >> Time at risk = 547035 >> LR chi2(7) = 6502.85 >> Log likelihood = -75116.825 Prob > chi2 = 0.0000 >> >> ------------------------------------------------------------------------------ >> _t | Coef. Std. Err. z P>|z| [95% Conf. Interval] >> -------------+---------------------------------------------------------------- >> mean_redu~m1 | 1.160556 .0260155 44.61 0.000 1.109567 1.211546 >> reductcur~m1 | 1.332635 .0276246 48.24 0.000 1.278492 1.386779 >> rent_net8_~m | .2179676 .0216012 10.09 0.000 .17563 .2603052 >> diff_stdma~m | .8829475 .0920925 9.59 0.000 .7024495 1.063446 >> diff_mortg~m | .2271822 .0913231 2.49 0.013 .0481921 .4061722 >> permanenti~m | -.0774641 .0212722 -3.64 0.000 -.1191569 -.0357713 >> appreciati~m | -.1104136 .0475282 -2.32 0.020 -.2035672 -.0172601 >> ------------------------------------------------------------------------------ >> >> . estat phtest,detail >> >> Test of proportional-hazards assumption >> >> Time: Time >> ---------------------------------------------------------------- >> | rho chi2 df Prob>chi2 >> ------------+--------------------------------------------------- >> mean_redu~m1| -0.29894 664.62 1 0.0000 >> reductcur~m1| -0.01441 2.31 1 0.1283 >> rent_net8_~m| -0.01523 2.21 1 0.1374 >> diff_stdma~m| -0.01545 0.10 1 0.7516 >> diff_mortg~m| -0.14583 6.94 1 0.0084 >> permanenti~m| 0.06388 39.67 1 0.0000 >> appreciati~m| 0.04365 17.29 1 0.0000 >> ------------+--------------------------------------------------- >> global test | 758.70 7 0.0000 >> ---------------------------------------------------------------- >> >> I wonder what is your opinion. We see here 3 stratifications, which >> makes the results of the Cox regression valid >> >> Thanks, Yuval >> >> On Fri, Jan 6, 2012 at 2:54 PM, Maarten Buis <maartenlbuis@gmail.com> wrote: >>>> On Fri, Jan 6, 2012 at 10:06 AM, Yuval Arbel <yuval.arbel@gmail.com> wrote: >>>>> My first question is whether this discussion [of the proportional hazard assumption, MB] is relevant if I am >>>>> applying the Cox model to describe the exercise of call (real) options >>>>> to purchase appartments. >>>>> >>>>> My second question is <snip>: is there any command to incorporate the -stcox- with >>>>> varying hazard level across time? I'm aware of the -strata()- option, >>>>> but I wonder whether I can somehow account for time-varying covariates >>>>> and incorporate it with -stcox- >>> >>> On Fri, Jan 6, 2012 at 9:33 AM, Yuval Arbel wrote: >>>> Note also that in the medical context, the treatment - is a binary >>>> variable, which equals 1 for the experimental treatment and 0 >>>> otherwise. >>>> In our context - the variable of interest is the reduction rate in >>>> percentage points - where this variable is quantitative. >>> >>> The proportional hazard assumption is required for Cox regression >>> regardless of whether you are dealing with medical or economic data, >>> the variables are binary or (pseudo-)continuous, or you have >>> experimental or observational data. >>> >>> I gave an example on how to estimate and interpret a Cox model in >>> which you relax the proportional hazard assumption by allowing the >>> effect to change over time here: >>> <http://www.stata.com/statalist/archive/2011-06/msg00358.html> >>> >>> Hope this helps, >>> Maarten >>> >>> -------------------------- >>> Maarten L. Buis >>> Institut fuer Soziologie >>> Universitaet Tuebingen >>> Wilhelmstrasse 36 >>> 72074 Tuebingen >>> Germany >>> >>> >>> http://www.maartenbuis.nl >>> -------------------------- >>> * >>> * For searches and help try: >>> * http://www.stata.com/help.cgi?search >>> * http://www.stata.com/support/statalist/faq >>> * http://www.ats.ucla.edu/stat/stata/ >> >> >> >> -- >> Dr. Yuval Arbel >> School of Business >> Carmel Academic Center >> 4 Shaar Palmer Street, >> Haifa 33031, Israel >> e-mail1: yuval.arbel@carmel.ac.il >> e-mail2: yuval.arbel@gmail.com >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ > > > > -- > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > > http://www.maartenbuis.nl > -------------------------- > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ -- Dr. Yuval Arbel School of Business Carmel Academic Center 4 Shaar Palmer Street, Haifa 33031, Israel e-mail1: yuval.arbel@carmel.ac.il e-mail2: yuval.arbel@gmail.com * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/