summary.probtrans {mstate}

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,
  times,
  from = 1,
  to = 0,
  variance = TRUE,
  conf.int = 0.95,
  conf.type = c("log", "none", "plain"),
  extend = FALSE,
  ...
)

Arguments

object

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

times

Time points at which to evaluate the transition probabilites

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. Default is print from state 1 only. User can specify from=0 to print transition probabilities from all states

to

Specifies the transition probabilities to which state are to be printed. User can specify to=0 to print transition probabilities to all states. This is also the default

variance

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

conf.int

The proportion to be covered by the confidence intervals, default is 0.95

conf.type

The type of confidence interval, one of "log", "none", or "plain". Defaults to "log"

extend

logical value: if TRUE, prints information for all specified times, even if there are no subjects left at the end of the specified times. This is only valid if the times argument is present

...

Further arguments to print

Value

Function summary.probtrans returns an object of class "summary.probtrans", which is a list (for each from state) of transition probabilities at the specified (or all) time points. The print method of a summary.probtrans doesn't return a value.

Author(s)

Hein Putter [e-mail]

See Also

probtrans

Examples

# First run the example of probtrans
tmat <- trans.illdeath()
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))
tglong <- msprep(time=c(NA,"illt","dt"),status=c(NA,"ills","ds"),
                 data=tg,keep=c("x1","x2"),trans=tmat)
tglong <- expand.covs(tglong,c("x1","x2"))
cx <- coxph(Surv(Tstart,Tstop,status)~x1.1+x2.2+strata(trans),
            data=tglong,method="breslow")
newdata <- data.frame(trans=1:3,x1.1=c(0,0,0),x2.2=c(0,1,0),strata=1:3)
HvH <- msfit(cx,newdata,trans=tmat)
pt <- probtrans(HvH,predt=0)

# Default, prediction from state 1
summary(pt)
# Only from states 1 and 3
summary(pt, from=c(1, 3))
# Use from=0 for prediction from all states
summary(pt, from=0)
# Only to states 1 and 2
summary(pt, to=1:2)
# Default is 95% confidence interval, change here to 90% 
summary(pt, to=1:2, conf.int=0.90)
# Do not show variances (nor confidence intervals)
summary(pt, to=1:2, variance=FALSE)
# Transition probabilities only at specified time points
summary(pt, times=seq(0, 15, by=3))
# Last specified time point is larger than last observed, not printed
# Use extend=TRUE as in summary.survfit
summary(pt, times=seq(0, 15, by=3), extend=TRUE)
# Different types of confidence intervals, default is log
summary(pt, times=seq(0, 15, by=3), conf.type="plain")
summary(pt, times=seq(0, 15, by=3), conf.type="no")
# When the number of time points specified is larger than 12, head and tail is shown
x <- summary(pt, times=seq(5, 8, by=0.25))
x