Dear Terry
 here is the survreg line from which I understand that "gender" is significant

survreg(formula = Surv(dias, status) ~ trat * sexo * rep, dist = "weibull") 
                   Value      Std. Error       z        p  
sexom            -0.2187     0.0993  -2.202 2.76e-02 

and the log rank result

>  survdiff(Surv(dias, status) ~ sexo) 
Call: 
survdiff(formula = Surv(dias, status) ~ sexo)  
         N Observed Expected (O-E)^2/E (O-E)^2/V 
sexo=h 458      458      472     0.397      1.83 
sexo=m 451      451      437     0.428      1.83  
 Chisq= 1.8  on 1 degrees of freedom, p= 0.176
 
do you think this could be an error code or is it because they are different models?

thank you very much
Eugenia


________________________________
 De: Terry Therneau-2 [via R] <ml-node+s789695n4551787h91@n4.nabble.com>
Para: mariaeugeniau <mariaeugeniau@yahoo.com.ar> 
Enviado: jueves, 12 de abril de 2012 10:06
Asunto: Re: Survreg output - interpretation


--- begin included message --- 
Hello R users, 
I am analizing survival data (mostly uncensored) and want to extract the 
most out of it. 
Since I have more than one factor, I?ve read that the survival regression 
can help to test the interactions between factors, and then decide how to do 
the comparisons using the Log-rank test (survdiff). 
1- if I chose the Weibull distribution, does the output inform the goodness
of fit to it? perhaps in this part of the output... 

Weibull distribution 
Loglik(model)= -1302.8   Loglik(intercept only)= -1311 
         Chisq= 16.49 on 11 degrees of freedom, p= 0.12 
Number of Newton-Raphson Iterations: 7 
n= 873 

2- one of my factors is "gender" (2 levels). With survreg, it appears as 
significant, but if I compare them with log-rank it turns not significant.
Are they comparing different things? or is it a test power issue? 

------- end inclusion ------- 

1. To understand goodness of fit you need to look at the residuals in 
multiple ways.  (The same answer applies to ordinary linear regression.) 

2. You have not given us enough information to answer the questions.  If 
the data is p=.049 vs p=.051, the the answers are in agreement even 
though the artificial label of "significant" changes.  The logrank test 
and survreg are not the same model.  If the data is p=.02 vs p=.8, then 
you have an error in the code. 

Terry Therneau 

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