Re: multivariate Cox model p values

arrayprofile@xxxxxxxxx wrote:
Hi, I encountered a situation when I ran Cox proportional regression
with 6 independent variables. If I ran the regression with each
independent variable separately (i.e. univariable analysis), only 1
variable is significant. when I ran the Cox regression with all
variables together in one model (i.e. multivariable analysis), not only
the variable significant in the univariable analysis is still
significant, but 2 of the other variables showed significant as well.
How to explain this?

I can imagine that a variable may appear insignificant in unvariable
analysis and disappeared in multivariable analysis because the effect
can be taken away by other correlated variables, but can't think of a
reason why the opposite can happen. Can anybody explain?

Thanks

John

Controlling for confounder can can make the observed effect smaller or larger. You might try searching on "positive and negative confounding" to find examples.

--
Bruce Weaver
bweaver@xxxxxxxxxxxx
www.angelfire.com/wv/bwhomedir
.

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