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#Two models
coxsst4 <- coxph(Surv(schaeden)~ S5, data=nino4)
coxsst4_full <- coxph(Surv(schaeden)~ 0+S1+S2+S3+S4+S5+S6+S7+S8+S9+S10,
data=nino4)
#Set all covariates 0
attach(nino4)
newS4 <- data.frame(S0=0., S1=0., S2=0., S3=0., S4=0., S5=0., S6=0.,
S7=0., S8=0., S9=0., S10=0.)
detach()
new_surv1 <- survfit(coxsst4, newdata=newS4)
new_surv2 <- survfit(coxsst4_full, newdata=newS4)
Yields two different curves. What did I get wrong?
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You did nothing wrong. As I have said before, the survival curve from a Cox
model is always for a particular hypothetical subject with a particular choice
of covariates: there is nothing special (nil, nada, zip, NOTHING) about a
covariate choice of zero. There is no such thing as "the" baseline survival
curve.
1. Imagine someone sabotaged your data set by replacing S1 with S1+6. None
of
the Cox model coefficients or inferences would change, but "0" is now someone
quite different than before.
2. Consider the linear models
fit1 <- lm(pat.karno ~ age, data=lung)
fit2 <- lm(pat.karno ~ age + sex, data=lung)
They have different predicted values for the hypothethical subject with
age=sex=0. (A subject with age=0 sex=0 is not particularly interesting of
course, but then coxph survival curves for all covariates=0 are about the same.)
A baseline curve for all zeros is essentially an intercept term, and since it
depends on what other covariates were or were not in the model is not useful on
its own.
Terry Therneau
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