mstate (version 0.2.6)

summary.probtrans: Summary method for a probtrans object

Description

Summary method for an object of class 'probtrans'. It prints a selection of the estimated transition probabilities, and, if requested, also of the variances.

Usage

## S3 method for class 'probtrans':
summary(object,from,complete=FALSE,variance=TRUE,...)

Arguments

object

Object of class 'probtrans', containing estimated transition probabilities from and to all states in a multi-state model

from

Specifies from which state the transition probabilities are to be printed. Should be subset of 1:S, with S the number of states in the multi-state model. If missing, transition probabilities are printed from all starting states

complete

Whether or not the complete estimated transition probabilities should be printed (TRUE) or not (FALSE); default is FALSE, in which case the estimated transition probilities will be printed for the first and

variance

Whether or not the standard errors of the estimated transition probabilities should be printed; default is TRUE

...

Further arguments to summary

Value

• No return value

See Also

probtrans

Examples

# transition matrix for illness-death model
tmat <- trans.illdeath()
# data in wide format, for transition 1 this is dataset E1 of
# Therneau & Grambsch (2000)
tg <- data.frame(illt=c(1,1,6,6,8,9),ills=c(1,0,1,1,0,1),
        dt=c(5,1,9,7,8,12),ds=c(1,1,1,1,1,1),
        x1=c(1,1,1,0,0,0),x2=c(6:1))
# data in long format using msprep
tglong <- msprep(time=c(NA,"illt","dt"),status=c(NA,"ills","ds"),
		data=tg,keep=c("x1","x2"),trans=tmat)
# events
events(tglong)
table(tglong$status,tglong$to,tglong$from)
# expanded covariates
tglong <- expand.covs(tglong,c("x1","x2"))
# Cox model with different covariate
cx <- coxph(Surv(Tstart,Tstop,status)~x1.1+x2.2+strata(trans),
	data=tglong,method="breslow")
summary(cx)
# new data, to check whether results are the same for transition 1 as
# those in appendix E.1 of Therneau & Grambsch (2000)
newdata <- data.frame(trans=1:3,x1.1=c(0,0,0),x2.2=c(0,1,0),strata=1:3)
msf <- msfit(cx,newdata,trans=tmat)
# probtrans
pt <- probtrans(msf,predt=0)
# default summary
summary(pt)
# summary without standard errors
summary(pt,variance=FALSE)