Dear list,I am quite new to the world of biostatistics and I encounter some
issues in the precise understanding of the coxph function of the survival
package.I have a set of survival data (patient who had (or died from) a
breast cancer) I'd like to see which are the variables that might cause dead
or not.When trying> summary(coxph(Surv(Time_to_distant_recurrence_yrs,
!Distant_recurrence)~  as.factor(Herceptincat) + as.factor(nodeCat_all) ,
data = her2.matrix))I obtain
Call:coxph(formula = Surv(Time_to_distant_recurrence_yrs,
!Distant_recurrence) ~     as.factor(Herceptincat) + as.factor(nodeCat_all),
data = her2.matrix)  n= 231, number of events= 53                            
coef exp(coef) se(coef)      z Pr(>|z|)   as.factor(Herceptincat)1 -0.5891   
0.5548   0.2805 -2.100  0.03570 * as.factor(nodeCat_all)1   0.9718    2.6426  
0.6195  1.569  0.11672   as.factor(nodeCat_all)2   1.9713    7.1803   0.6101 
3.231  0.00123 **---Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1
‘ ’ 1                          exp(coef) exp(-coef) lower .95 upper
.95as.factor(Herceptincat)1    0.5548     1.8025    0.3202   
0.9614as.factor(nodeCat_all)1     2.6426     0.3784    0.7847   
8.8989as.factor(nodeCat_all)2     7.1803     0.1393    2.1720  
23.7368Concordance= 0.699  (se = 0.039 )Rsquare= 0.096   (max possible=
0.911 )Likelihood ratio test= 23.33  on 3 df,   p=3.444e-05Wald test           
= 21.76  on 3 df,   p=7.312e-05Score (logrank) test = 24.46  on 3 df,  
p=2.001e-05
I think, this means that the two variables I tested are of interest to build
the model with a p-value of  about 10e-5. This also mean that having the
Herceptincat =1 is significantly different from having a Herceptincat = 0.
Moreover, having a nodecat_all iof 2 is significantly different from having  
a nodecat equals to 0.However, this does not tell me much about the
variables alone. 
summary(coxph(Surv(Time_to_distant_recurrence_yrs, !Distant_recurrence)~ 
as.factor(Herceptincat) , data = her2.matrix))Call:coxph(formula =
Surv(Time_to_distant_recurrence_yrs, !Distant_recurrence) ~    
as.factor(Herceptincat), data = her2.matrix)  n= 231, number of events= 53                            
coef exp(coef) se(coef)      z Pr(>|z|)as.factor(Herceptincat)1 -0.4326   
0.6488   0.2789 -1.551    0.121                         exp(coef) exp(-coef)
lower .95 upper .95as.factor(Herceptincat)1    0.6488      1.541    0.3756    
1.121Concordance= 0.568  (se = 0.035 )Rsquare= 0.011   (max possible= 0.911
)Likelihood ratio test= 2.45  on 1 df,   p=0.1177Wald test            = 2.41 
on 1 df,   p=0.1208Score (logrank) test = 2.44  on 1 df,   p=0.118
So, if I only consider one variable (Herceptincat). This does not seem very
interesting. However, nodecat_all is far more interesting.Does it mean that
nodecat_all is enough for me to build a model and that I don't have to take 
(Herceptincat).  into account? I am bit lost between the global p-values and
those that accont for only one variables. It is even worse with factorized
variables because, you miss a p-value for the first category!I thank you all
for your help!
Call:coxph(formula = Surv(Time_to_distant_recurrence_yrs,
!Distant_recurrence) ~     as.factor(nodeCat_all), data = her2.matrix)  n=
231, number of events= 53                           coef exp(coef) se(coef)    
z Pr(>|z|)   as.factor(nodeCat_all)1 0.8525    2.3455   0.6173 1.381 
0.16726   as.factor(nodeCat_all)2 1.8288    6.2264   0.6068 3.014  0.00258
**---Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1                        
exp(coef) exp(-coef) lower .95 upper .95as.factor(nodeCat_all)1     2.346    
0.4263    0.6995     7.865as.factor(nodeCat_all)2     6.226     0.1606   
1.8956    20.452Concordance= 0.661  (se = 0.036 )Rsquare= 0.078   (max
possible= 0.911 )Likelihood ratio test= 18.84  on 2 df,   p=8.111e-05Wald
test            = 17.26  on 2 df,   p=0.0001787Score (logrank) test = 19.9 
on 2 df,   p=4.785e-05




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