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st: RE: Proportional hazard assumption for interaction term


From   "Kieran McCaul" <Kieran.McCaul@uwa.edu.au>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Proportional hazard assumption for interaction term
Date   Tue, 25 May 2010 08:25:23 +0800

....

OK, I'll have a go at this.

(1) In your model, you've fitted thincat99, which as you stated, indicates missing thincat values.  You've also fitted stage9 which I assume means missing stage.
You can't treat missing values like this in a model because they are not a separate category, they are some unknown mixture of the categories.  So you either drop them from the model or use imputation to impute the missing data.  You need to deal with this first before you start worrying about proportionality assumptions.

(2) Some of the variables with significant p-values from the phtest also have very small rho values.  This is the case for thincat5, which is the effect of thincat in yearcat1.  I'm guessing you have a large dataset with a large number of events observed, so the phtest will have power to detect small non-proportionality effects.  You need to plot the Schoenfeld residuals over time, overlayed with a lowess smoother, to see what is going on here. This is explained in the Stata manual.

(3) The lack of proportionality with the stage variable is not surprising if, as I suspect, this is a cancer dataset and the outcome is death or recurrence or both.  Stage is a fairly crude classification and there will still be a fair degree of heterogeneity of risk within each of these categories.  So, early in the course of follow-up, those most at risk in each stage category fail first, and over time the heterogeneity in risk within each stage category is reduced.  This happens relatively quickly, with a year or so after diagnosis, and is the source of the lack of proportionality.  If you graph the hazard curves you'll see what I mean.  How you deal with this depends on what you want from the model.  If you aren't interested in the stage-specific hazard ratios then you could simply stratify on stage.

	
-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Sanam P
Sent: Monday, 24 May 2010 8:23 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: Proportional hazard assumption for interaction term

Dear Statalist

I was wondering if in the Cox model there is interaction between two categorical variables (here thincat and yearcat), when checking the proportional hazard assumption using :
"estat phtest,rank detail"  after the model, for the interaction term and the variables involved in interaction to be proportional, should all the terms involved in the interaction be unsignificant? if only one of the main effects at reference value of the other variable involved in the interaction term is significant and all other terms involved in the interaction are not significant, does it still mean that variable is not proportional? is the interaction proportional?
(thincat=99 is a category with missing thincat values) 
 
Thank you very much in advance,
Sanam
 
      Time:  Rank(t)
      ----------------------------------------------------------------
                  |       rho            chi2       df       Prob>chi2
      ------------+---------------------------------------------------
      _Ithincat~2|      0.00850         0.38        1         0.5365
      _Ithincat~3|      0.00776         0.32        1         0.5725
      _Ithincat~4|     -0.01565         1.30        1         0.2546
      _Ithincat~5|     -0.04559        11.07        1         0.0009
      _Ithinca~99|     -0.04752        11.73        1         0.0006
      _Iyearcat_2  |     -0.00202         0.02        1         0.8835
      _Iyearcat_3  |      0.00318         0.05        1         0.8179
      _Iyearcat_4  |     -0.00582         0.18        1         0.6727
      _IthiXye~2_2|     -0.00935         0.46        1         0.4964
      _IthiXye~2_3|     -0.00543         0.16        1         0.6928
      _IthiXye~2_4|      0.00142         0.01        1         0.9179
      _IthiXye~3_2|     -0.00478         0.12        1         0.7281
      _IthiXye~3_3|     -0.00690         0.25        1         0.6158
      _IthiXye~3_4|     -0.00593         0.19        1         0.6659
      _IthiXye~4_2|     -0.00334         0.06        1         0.8081
      _IthiXye~4_3|     -0.00142         0.01        1         0.9177
      _IthiXye~4_4|      0.00910         0.44        1         0.5083
      _IthiXye~5_2|      0.00079         0.00        1         0.9543
      _IthiXye~5_3|      0.01261         0.84        1         0.3594
      _IthiXye~5_4|      0.01758         1.63        1         0.2013
      _IthiXye~9_2|      0.00488         0.13        1         0.7229
      _IthiXye~9_3|      0.01125         0.67        1         0.4141
      _IthiXye~9_4|      0.02605         3.58        1         0.0584
      _Istage_2   |     -0.07686        33.18        1         0.0000
      _Istage_3   |     -0.12396        82.46        1         0.0000
      _Istage_99  |     -0.01765         1.65        1         0.1995
      _Ihepcat_2 |     -0.02429         3.22        1         0.0727
      _Ihepcat_3 |      0.02983         4.73        1         0.0296
      _Ihepcat_4 |     -0.02254         2.58        1         0.1085
      _Ihepcat_5 |      0.03376         6.11        1         0.0135
      _Ihepcat_6 |      0.01624         1.43        1         0.2324
      _Ihepcat_7 |      0.00075         0.00        1         0.9562
      _Isitecat_2 |     -0.02876         4.56        1         0.0327
      _Isitecat_3 |     -0.02031         2.24        1         0.1345
      _Isitecat_4 |     -0.01405         1.09        1         0.2973
      _Isitecat_5 |     -0.11526        65.59        1         0.0000
      _Isexcat_1  |      0.01412         1.08        1         0.2979
      _Iseason_2  |      0.01397         1.03        1         0.3092
      _Iseason_3  |      0.00282         0.04        1         0.8368
      _Iseason_4  |      0.02057         2.24        1         0.1344
      agecatf     |     -0.00911         0.49        1         0.4843
      ------------+---------------------------------------------------
      global test |                    976.68       41         0.0000
      ----------------------------------------------------------------



      


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