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options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly'),
show.signif.stars=FALSE) #ensure constrast type
library(survival)
#
# Fit the models found in Wei et. al.
#
wfit <- coxph(Surv(stop, event) ~ (rx + size + number)* strata(enum),
cluster=id, bladder, ties='breslow')
wfit
# Check the rx coefs versus Wei, et al, JASA 1989
rx <- c(1,4,5,6) # the treatment coefs above
cmat <- diag(4); cmat[1,] <- 1; #contrast matrix
wfit$coefficients[rx] %*% cmat # the coefs in their paper (table 5)
t(cmat) %*% wfit$var[rx,rx] %*% cmat # var matrix (eqn 3.2)
# Anderson-Gill fit
fita <- coxph(Surv(start, stop, event) ~ rx + size + number, cluster=id,
bladder2, ties='breslow')
summary(fita)
# Prentice fits. Their model 1 a and b are the same
fit1p <- coxph(Surv(stop, event) ~ rx + size + number, bladder2,
subset=(enum==1), ties='breslow')
fit2pa <- coxph(Surv(stop, event) ~ rx + size + number, bladder2,
subset=(enum==2), ties='breslow')
fit2pb <- coxph(Surv(stop-start, event) ~ rx + size + number, bladder2,
subset=(enum==2), ties='breslow')
fit3pa <- coxph(Surv(stop, event) ~ rx + size + number, bladder2,
subset=(enum==3), ties='breslow')
#and etc.
fit1p
fit2pa
fit2pb
fit3pa
rm(rx, cmat, wfit, fita, fit1p, fit2pa, fit2pb, fit3pa)
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