R: Compute subject-specific or overall transition probabilities...

probtrans {mstate} | R Documentation

## 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 transitions
`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`=`"fixedhorizon"`)
`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`)

### Details

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

### Value

An object of class `"probtrans"`, which is a list of which item [[s]] contains a data frame with the estimated transition probabilities (and standard errors if `variance`=`TRUE`) from state s. If `covariance`=`TRUE`, item `varMatrix` contains an array of dimension K^2 x K^2 x (nt+1) (with K the number of states and nt the distinct transition time points); the time points correspond to those in the data frames with the estimated transition probabilities. Finally, there are items `trans`, `method`, `predt`, `direction`, recording the transition matrix, and the method, predt and direction arguments used in the call to probtrans. Plot and summary methods have been defined for `"probtrans"` objects.

### Author(s)

Liesbeth de Wreede and Hein Putter H.Putter@lumc.nl

### 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.

de Wreede LC, Fiocco M, and Putter H (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.

de Wreede LC, Fiocco M, and Putter H (2011). mstate: An R Package for the Analysis of Competing Risks and Multi-State Models. Journal of Statistical Software, Volume 38, Issue 7.

### 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]]

[Package mstate version 0.3.1 Index]