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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 | Sat, 7 Jan 2012 05:58:36 +0200 |
Thanks very much Alex, By scaling to hazard, it worked fine. I'm very
satisfied with the results, which are robust to -stcox- and I
incorporated an additional footnote in the paper draft. I'm generally
familiar with the cubic-spline method, which allows time variations,
but also permits well-defined derivatives at the knots. Here is the
outputs:
. doedit
. do "D:\kingston\public_housing\robustness_PH_assumption.do"
. clear
. clear matrix
. set memory 500m
(512000k)
. set matsize 800
. use "g:\public housing\test_sample_May_07_Bought.dta", clear
.
.
. stpm2 mean_reduct reductcurrent_mean_reduct rent_net8
diff_stdmadadarea diff_mortgage permanentincomeestimate82 a
> ppreciation,df(4) scale(hazard)
note: delayed entry models are being fitted
Iteration 0: log likelihood = -1512.9266
Iteration 1: log likelihood = -1378.564
Iteration 2: log likelihood = -1375.8226
Iteration 3: log likelihood = -1375.8201
Iteration 4: log likelihood = -1375.8201
Log likelihood = -1375.8201 Number of obs = 499393
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
xb |
mean_reduct | .0380149 .0005223 72.79 0.000 .0369912 .0390385
reductcurr.. | .0209263 .0004782 43.76 0.000 .019989 .0218637
rent_net8 | .0027275 .0001649 16.54 0.000 .0024043 .0030508
diff_stdma~a | -.2068624 .0283549 -7.30 0.000 -.262437 -.1512879
diff_mortg~e | -9.819042 .5364728 -18.30 0.000 -10.87051 -8.767574
permanent~82 | -.0005591 .0000685 -8.16 0.000 -.0006933 -.0004248
appreciation | 23.16299 2.165592 10.70 0.000 18.91851 27.40747
_rcs1 | 5.573527 .1047814 53.19 0.000 5.368159 5.778895
_rcs2 | 1.805992 .0491048 36.78 0.000 1.709749 1.902236
_rcs3 | -.34599 .0112847 -30.66 0.000 -.3681077 -.3238723
_rcs4 | -.076871 .0026999 -28.47 0.000 -.0821628 -.0715792
_cons | -5.34359 .1040326 -51.36 0.000 -5.54749 -5.13969
------------------------------------------------------------------------------
. test mean_reduct==reductcurrent_mean_reduct
( 1) [xb]mean_reduct - [xb]reductcurrent_mean_reduct = 0
chi2( 1) = 947.55
Prob > chi2 = 0.0000
. stcox mean_reduct reductcurrent_mean_reduct rent_net8
diff_stdmadadarea permanentincomeestimate82 diff_mortgage a
> ppreciation,nohr
failure _d: fail == 1
analysis time _t: time_index
id: appt
Iteration 0: log likelihood = -78368.249
Iteration 1: log likelihood = -74721.874
Iteration 2: log likelihood = -74566.501
Iteration 3: log likelihood = -74561.567
Iteration 4: log likelihood = -74561.555
Refining estimates:
Iteration 0: log likelihood = -74561.555
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) = 7613.39
Log likelihood = -74561.555 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mean_reduct | .0353358 .0005278 66.94 0.000 .0343012 .0363703
reductcurr.. | .0221957 .0005134 43.23 0.000 .0211894 .0232019
rent_net8 | .0025506 .0001655 15.41 0.000 .0022263 .0028749
diff_stdma~a | -.4642809 .0446886 -10.39 0.000 -.5518688 -.3766929
permanent~82 | -.0004675 .0000689 -6.79 0.000 -.0006025 -.0003325
diff_mortg~e | -6.430141 .8913818 -7.21 0.000 -8.177217 -4.683064
appreciation | 9.629971 3.161657 3.05 0.002 3.433237 15.8267
------------------------------------------------------------------------------
. test mean_reduct==reductcurrent_mean_reduct
( 1) mean_reduct - reductcurrent_mean_reduct = 0
chi2( 1) = 450.86
Prob > chi2 = 0.0000
. estat phtest,detail
Test of proportional-hazards assumption
Time: Time
----------------------------------------------------------------
| rho chi2 df Prob>chi2
------------+---------------------------------------------------
mean_reduct | -0.18814 220.31 1 0.0000
reductcurr..| -0.21984 436.16 1 0.0000
rent_net8 | -0.03327 10.10 1 0.0015
diff_stdma~a| 0.03801 0.38 1 0.5357
permanent~82| -0.01174 1.35 1 0.2459
diff_mortg~e| 0.21517 10.31 1 0.0013
appreciation| -0.05323 11.87 1 0.0006
------------+---------------------------------------------------
global test | 543.71 7 0.0000
----------------------------------------------------------------
On Fri, Jan 6, 2012 at 11:46 PM, Alex Gamma <alex.gamma@uzh.ch> wrote:
> Yuval,
>
> provided that you -stset- your data correctly (i.e. as containing delyed entries), stpm2 obviously requires you to specifiy the scale option in order to estimate such models. Apart from the command's help-file, there is also a paper from The Stata Journal that explains the use of stpm2 in detail.
>
> Paul C. Lambert & Patrick Royston
> Further development of flexible parametric models for survival analysis
> The Stata Journal (2009) 9, Number 2, pp. 265–290
>
> Alex
>
>
>
>> Thanks, that sounds great.
>>
>> I tried this and got the following error command:
>>
>> . stpm2 mean_reduct reductcurrent_mean_reduct rent_net8
>> diff_stdmadadarea diff_mortgage permanentincomeestimate82 a
>>> ppreciation,df(4)
>> note: delayed entry models are being fitted
>> The scale must be specified
>>
>> Note that in my sample - tenants start to exercise at t=13. Is this
>> fact has something to do with this error message?
>>
>> On Fri, Jan 6, 2012 at 5:14 PM, Alex Gamma <alex.gamma@uzh.ch> wrote:
>>> Hi Yuval,
>>>
>>> I prefer the user-written command STPM2 for these kinds of situation. It makes it easy to model variables that violate the PH-assumption as time-dependent effects using cubic splines.
>>>
>>> - ssc describe stpm2 -
>>> - ssc install stpm2 -
>>>
>>> Alex
>>>
>>>
>>> Am 06.01.2012 um 09:06 schrieb Yuval Arbel:
>>>
>>>> Dear Statalist Participants,
>>>>
>>>> I'm working with stata 11.2. Having read carefully stata's manual
>>>> under the title "stcox PH-assumption tests" I have two questions
>>>> (which seems to be relevant to Marteen's answer in another thread):
>>>>
>>>> The manual shows very nicely the following situation related to
>>>> medical experiments: if we take two groups of cancer patients, where
>>>> one group is exposed to a standard treatment and the other to a
>>>> special treatment - and we would like to show that the experimental
>>>> treatment is more efficient, we anticipate a paralel upward shift of
>>>> the projected survival rates compared to the actual ones. If this is
>>>> the case - the PH-assumption, namely the assumption that the hazard to
>>>> survival is constant over the sample period, is supported
>>>> statistically.
>>>>
>>>> My first question is whether this discussion is relevant if I am
>>>> applying the Cox model to describe the exercise of call (real) options
>>>> to purchase appartments.
>>>>
>>>> My second question is the following: suppose that the PH-assumption
>>>> does not hold in the sample and the above discussion is relevant. The
>>>> stata manual says the following: "If the assumption fails, alternative
>>>> modeling choices would be more appropriate (e.g. , a stratified Cox
>>>> model, time-varying covariates)."
>>>>
>>>> The question is: 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-
>>>>
>>>> --
>>>> 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/
>>>
>>>
>>> *
>>> * 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/
>
>
> *
> * 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/