# Violation of proportional hazard assumption with big sample size - how to correct for it?

I am looking at a sample of about 95,000 firms in Switzerland and I want to find out if having an external auditor (such as PwC, KPMG, EY) reduces the likelihood that a firm goes bankrupt. In Switzerland, smaller companies can choose if they want to have their accounts audited or not, i.e. they can 'opt-out' from having an external auditor.

So far I have looked at this mainly by using logistic regression models with bankruptcy (0/1) being the dependent variable. However, I would like to corroborate my results with survival analysis techniques.

I have come up with the following basic Cox proportional hazard model in Stata:

stcox bOptingOut lncapital i.firmCanton i.industryCode

• bOptingOut is my treatment/non-treatment variable (1 = opting-out, i.e. no auditor; 0 = no opting-out, i.e. financial statements are audited)
• lncapital is the natural logarithm of the firm's paid up capital in Swiss Francs (corresponds practically to USD)
• firmCanton is an indicator variable to control for the canton/state in which the firm is domiciled
• industryCode is an indicator variable to control for industry effects

In principle, I was quite happy with the model as the coefficients are highly significant and the direction of the effects appears reasonable.

. stcox i.bOptingOut lncapital i.firmCanton i.industryCode, nolog

failure _d:  event == 3
analysis time _t:  (date1-origin)
origin:  event==1
enter on or after:  event==1 time td(01jan2008)
exit on or before:  event==3 time td(31dec2018)
id:  id

Cox regression -- Breslow method for ties

No. of subjects =       94,319                  Number of obs    =     161,050
No. of failures =        9,683
Time at risk    =    299931461
LR chi2(44)      =     4529.30
Log likelihood  =   -106232.09                  Prob > chi2      =      0.0000

------------------------------------------------------------------------------
_t | Haz. Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.bOptingOut |   1.425763   .0464468    10.89   0.000     1.337574    1.519766
lncapital |   .8598573   .0107152   -12.12   0.000     .8391103    .8811172


Then I turned to to testing the proportional hazards assumption. First, I did some graphical checks and plotted the hazard and survival functions as well as stphplot and stcoxkm. Things do look quite okay (see picture below).

Afterwards, I performed the test based on Schoenfeld residuals (estat phtest) and get a Prob>chi2 value of 0.0000. Based on this, the proportional hazards assumption is clearly violated.

Currently, I have two main questions:

a) Does my data set really violate the proportional hazard (PH) assumption? Or is this simply due to the big sample size I have?

b) If the PH assumption is really violated, is it okay to use a lognormal parametric model instead [streg ..., dist(lognormal)]? Based on AIC/BIC that would be the best of the parametric models.

EDIT: Below I attach a plot of the Schoenfeld resiudals (Stata command estat phtest, plot(bOptingOut))

• I don't know Stata, but couldn't you output a plot of Schoenfeld residuals like in plot.zph in R ? Another good way to visualize if the PH assumption is violated is to split the model on several periods (survSplit function in R) and see if HR are very different, is that doable with Stata ? There indeed is a probability that your Schoenfeld residuals test is significant because of your big sample size but no one can tell without investigation – Dan Chaltiel Mar 27 '19 at 13:00
• Hi Dan Chaltiel, I edited the post and added a plot of Schoenfeld residuals. – Daniel Baettig Apr 3 '19 at 18:42