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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 15:23:50 +0200 |

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/

**Follow-Ups**:**Re: st: stcox in case the ph-assumption is rejected***From:*Maarten Buis <maartenlbuis@gmail.com>

**References**:**st: stcox in case the ph-assumption is rejected***From:*Yuval Arbel <yuval.arbel@gmail.com>

**Re: st: stcox in case the ph-assumption is rejected***From:*Yuval Arbel <yuval.arbel@gmail.com>

**Re: st: stcox in case the ph-assumption is rejected***From:*Maarten Buis <maartenlbuis@gmail.com>

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