mstate (version 0.2.6)

probtrans: Compute subject-specific or overall transition probabilities with standard errors

Description

This function computes subject-specific or overall transition probabilities in multi-state models. If requested, also standard errors are calculated.

Usage

probtrans(object, predt, direction=c("forward","fixedhorizon"),
          method=c("aalen","greenwood"), variance=TRUE, covariance=FALSE)

Arguments

object

msfit object containing estimated cumulative hazards for each of the transitions in the multi-state model and, if standard errors are requested, (co)variances of these cumulative hazards for each pair of transiti

predt

A positive number indicating the prediction time. This is either the time at which the prediction is made (if direction= "forward") or the time for which the prediction is to be made (if direction="fix

direction

One of "forward" (default) or "fixedhorizon", indicating whether prediction is forward or for a fixed horizon

method

A character string specifying the type of variances to be computed (so only needed if either variance or covariance is TRUE). Possible values are "aalen" or "greenwood"

variance

Logical value indicating whether standard errors are to be calculated (default is TRUE)

covariance

Logical value indicating whether covariances of transition probabilities for different states are to be calculated (default is FALSE)

Value

• An object of class "probtrans", which is a list containing S (if covariance=FALSE) or S+1 items (if covariance=TRUE), S being the number of states in the multi-state model. Item [[s]] contains a dataframe with the estimated transition probabilities from state s. If covariance=TRUE, item [[S+1]] contains an array with all the covariances of the estimated transition probabilities. The "probtrans" object has an attribute "trans" containing the transition matrix. Plot and summary methods have been defined for "probtrans" objects.

Details

For details refer to de Wreede, Fiocco & Putter (2010).

References

Andersen PK, Borgan O, Gill RD, Keiding N (1993). Statistical Models Based on Counting Processes. Springer, New York. Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 26, 2389--2430. Therneau TM, Grambsch PM (2000). Modeling Survival Data: Extending the Cox Model. Springer, New York. L. C. de Wreede, M. Fiocco, and H. Putter (2010). The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Computer Methods and Programs in Biomedicine 99: 261--274.

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)
HvH <- msfit(cx,newdata,trans=tmat)
# probtrans
pt <- probtrans(HvH,predt=0)
# predictions from state 1
pt[[1]]