logitSurv: Proportional odds survival model

In mets: Analysis of Multivariate Event Times

Proportional odds survival model

Description

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.

Usage

logitSurv(formula, data, offset = NULL, weights = NULL, ...)

Arguments

formula	formula with 'Surv' outcome (see coxph)
data	data frame
offset	offsets for exp(x beta) terms
weights	weights for score equations
...	Additional arguments to lower level funtions

Details

This is equivalent to using a hazards model

Z λ(t) \exp(x β)

where Z is gamma distributed with mean and variance 1.

Author(s)

Thomas Scheike

References

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,

Examples

data(TRACE)
dcut(TRACE) <- ~.
out1 <- logitSurv(Surv(time,status==9)~vf+chf+strata(wmicat.4),data=TRACE)
summary(out1)
gof(out1)
plot(out1)

mets documentation built on Jan. 17, 2023, 5:12 p.m.