Proportional odds survival model
Semiparametric Proportional odds model, that has the advantage that
logit(S(t|x)) = \log(Λ(t)) + x β
so covariate effects give OR of survival.
logitSurv(formula, data, offset = NULL, weights = NULL, ...)
formula |
formula with 'Surv' outcome (see |
data |
data frame |
offset |
offsets for exp(x beta) terms |
weights |
weights for score equations |
... |
Additional arguments to lower level funtions |
This is equivalent to using a hazards model
Z λ(t) \exp(x β)
where Z is gamma distributed with mean and variance 1.
Thomas Scheike
The proportional odds cumulative incidence model for competing risks, Eriksson, Frank and Li, Jianing and Scheike, Thomas and Zhang, Mei-Jie, Biometrics, 2015, 3, 687–695, 71,
data(TRACE) dcut(TRACE) <- ~. out1 <- logitSurv(Surv(time,status==9)~vf+chf+strata(wmicat.4),data=TRACE) summary(out1) gof(out1) plot(out1)
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