Hello,
Thanks a lot for all your answers!
My precedent mail is at the end of this one
Several among you asked me for precisions about my data and the aim of my
study.
You're right, I wasn't precise enough in my preceding mail!
I study the causes of a bone marrow graft rejection.
Therefore, I search genetic and clinical factors that we must particularly
look at for a graft, to reduce rejection, and to improve patient's survival.
Currently in my study, I have 2 continuous factors very correlated, that
both have an effect in survival
Methodology:
we have censored data, so I've done a Kaplan-Meier univariate analysis and
then a Cox multivariate analysis.
Y= duration of survival
X1= factor1
X2=factor2
Y=X1+X2+other factors
I know that I don't have to put both factors in the multivariate model
(because of the high correlation), but "I would like to" because the first
one is already known like having an effect on survival (it is the age of the
patient and it wouldn't be realist not to put it in the model), so my
interest is to know if the second factor has an effect too (independantly of
age), or if the effect that I see is just a consequence of the correlation
with the age
Someone among you propose to store the residuals of the 2 regressions: age
with factor2, and factor 2 with age, and use these residuals int the KM
analysis
I should try it!
Someone else suggest me to look at the partial correlation but my third
"factor" is qualitative (alive/dead) so I can't
another response:
"One further analysis you could do is to examine the effect of one
factor when the other factor is held constant, that is, perform a nested
analysis."
another response:
"By looking statistically at the subgroup of those who deviate from the
population trend, one may well be able to get some sort of handle on the
relative 'importance' of the two factors in determining the
outcome. "
Somone else wrote me that the most important was the colinearity (and
multicolinearity) and not the correlation.
But colinearity is a particular case of correlation?????
So do you think that there is no problem to put both factors in a Cox model
if there is high correlation but little colinearity????????????????????
I'll look at all those suggestions!
Thanks
Marie-Lorraine
At 09:30 21/01/2004, Marie-Lorraine APPERT wrote:
>Hello,
>
>I have problems with high correlated factors:
>I am working with censored data and I am looking for the factors (clinical
>or genetic) the most important for the survival of the patient.
>
>What can I do when two factors are highly correlated (|Pearson
coeff|>=0.7),
>and when I see that they both have an effect on survival?(in univariate
>analysis like Kaplan Meier)
>How can I know which one is really important for survival (I mean, which is
>the cause), and which one is just related to survival because of the
>correlation with the first factor (I mean, which is the consequence)?
>>Thanks for help
>
>ML. APPERT
|