R/probtrans.R
probtrans.RdThis function computes subject-specific or overall transition probabilities in multi-state models. If requested, also standard errors are calculated.
probtrans( object, predt, direction = c("forward", "fixedhorizon"), method = c("aalen", "greenwood"), variance = TRUE, covariance = FALSE )
| 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 | One of |
| method | A character string specifying the type of variances to be
computed (so only needed if either |
| variance | Logical value indicating whether standard errors are to be
calculated (default is |
| covariance | Logical value indicating whether covariances of transition
probabilities for different states are to be calculated (default is
|
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.
For details refer to de Wreede, Fiocco & Putter (2010).
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.
Liesbeth de Wreede and Hein Putter H.Putter@lumc.nl
# 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)#> $Frequencies #> to #> from healthy illness death no event total entering #> healthy 0 4 2 0 6 #> illness 0 0 4 0 4 #> death 0 0 0 6 6 #> #> $Proportions #> to #> from healthy illness death no event #> healthy 0.0000000 0.6666667 0.3333333 0.0000000 #> illness 0.0000000 0.0000000 1.0000000 0.0000000 #> death 0.0000000 0.0000000 0.0000000 1.0000000 #>#> , , = 1 #> #> #> 2 3 #> 0 2 4 #> 1 4 2 #> #> , , = 2 #> #> #> 2 3 #> 0 0 0 #> 1 0 4 #># 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)#> Call: #> coxph(formula = Surv(Tstart, Tstop, status) ~ x1.1 + x2.2 + strata(trans), #> data = tglong, method = "breslow") #> #> n= 16, number of events= 10 #> #> coef exp(coef) se(coef) z Pr(>|z|) #> x1.1 1.4753 4.3723 1.2557 1.175 0.240 #> x2.2 0.8571 2.3563 0.8848 0.969 0.333 #> #> exp(coef) exp(-coef) lower .95 upper .95 #> x1.1 4.372 0.2287 0.3731 51.24 #> x2.2 2.356 0.4244 0.4160 13.35 #> #> Concordance= 0.781 (se = 0.077 ) #> Likelihood ratio test= 2.93 on 2 df, p=0.2 #> Wald test = 2.32 on 2 df, p=0.3 #> Score (logrank) test = 2.86 on 2 df, p=0.2 #># 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]]#> time pstate1 pstate2 pstate3 se1 se2 se3 #> 1 0 1.0000000 0.00000000 0.000000000 0.00000000 0.00000000 0.00000000 #> 2 1 0.9299816 0.06204689 0.007971491 0.08750408 0.08250577 0.02915069 #> 3 5 0.9299816 0.00000000 0.070018378 0.08750408 0.00000000 0.08750408 #> 4 6 0.6776902 0.25229141 0.070018378 0.22689332 0.21903899 0.08750408 #> 5 7 0.6776902 0.12614570 0.196164083 0.22689332 0.12638316 0.14680748 #> 6 8 0.4757726 0.12614570 0.398081646 0.23614089 0.12638316 0.19877253 #> 7 9 0.0000000 0.47577265 0.524227350 0.00000000 0.53115171 0.53115171 #> 8 12 0.0000000 0.00000000 1.000000000 0.00000000 0.00000000 0.00000000