R help - Apr 2014 - Trend test for hazard ratios

Hello,

I have the following problem. I stratified my patient cohort into three
ordered groups and performed multivariate adjusted Cox regression analysis
on each group separately. Now I would like to calculate a p for trend across
the hazard ratios that I got for the three groups. How can I do that if I
only have the HR and the confidence interval? For example I got the
following HRs for one endpoint: 

1.09(0.68-1.74),	1.29(0.94-1.76) and 1.64(1.01-2.68). 

There is a trend but how do I calculate if it is significant?

Best regards

Marcus Kleber



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Hi Terry,

thank you for your suggestion. I tried to do it the way that you described.
I fitted my Cox model with strata and I get coefficients and P values for my
ZlnGalectin in the different strata (see summary below). So am not so
familiar with R so please excuse my perhaps stupid questions but are the P
values below like "p=2.386e-06" something similar like my P for trend
(they
seem to be very low?) or if not how can I calculate it now? 

> summary(fit_mort)
Call:
coxph(formula = Surv(obstime2010, death2010) ~ ZlnGalectin3 * 
    strata(CKD_KDIGO), data = gal.df)

  n= 2573, number of events= 758 
   (743 observations deleted due to missingness)


coef       exp(coef)   se(coef)      z    Pr(>|z|)
ZlnGalectin3
0.20725   1.23029  0.07064  2.934   0.00335 **
ZlnGalectin3:strata(CKD_KDIGO)CKD_KDIGO=60,01 - 90,00  0.04165   1.04253 
0.09178  0.454   0.65000 
ZlnGalectin3:strata(CKD_KDIGO)CKD_KDIGO=90,01+           -0.06830   0.93398 
0.12113 -0.564  0.57282 
                                                                              
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1


exp(coef) exp(-coef) lower .95  upper .95
ZlnGalectin3
1.230     0.8128    1.0712   1.413
ZlnGalectin3:strata(CKD_KDIGO)CKD_KDIGO=60,01 - 90,00     1.043     0.9592   
0.8709   1.248
ZlnGalectin3:strata(CKD_KDIGO)CKD_KDIGO=90,01+              0.934     1.0707   
0.7366   1.184
                                                                      
Concordance= 0.559  (se = 0.017 )
Rsquare= 0.011   (max possible= 0.978 )
Likelihood ratio test = 28.87  on 3 df,   p=2.386e-06
Wald test               = 28.64  on 3 df,   p=2.665e-06
Score (logrank) test = 28.46  on 3 df,   p=2.906e-06



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