This function computes subject-specific or overall cumulative transition hazards for each of the possible transitions in the multi-state model. If requested, also the variances and covariances of the estimated cumulative transition hazards are calculated.
msfit( object, newdata, variance = TRUE, vartype = c("aalen", "greenwood"), trans )
| object | A |
|---|---|
| newdata | A data frame with the same variable names as those that
appear in the |
| variance | A logical value indicating whether the (co-)variances of the
subject-specific transition hazards should be computed. Default is
|
| vartype | A character string specifying the type of variances to be
computed (so only needed if |
| trans | Transition matrix describing the states and transitions in the
multi-state model. See |
An object of class "msfit", which is a list containing
A data frame with time, Haz, trans,
containing the estimated subject-specific hazards for each of the
transitions in the multi-state model
A data frame with
time, Haz, trans1, trans2 containing the
variances (trans1=trans2) and covariances
(trans1<trans2) of the estimated hazards. This element is only
returned when variance=TRUE
The transition matrix used
The data frame needs to have one row for each transition in the multi-state
model. An additional column strata (numeric) is needed to describe
for each transition to which stratum it belongs. The name has to be
strata, even if in the original coxph call another variable
was used. For details refer to de Wreede, Fiocco & Putter (2010). So far,
the results have been checked only for the "breslow" method of
dealing with ties in coxph, so this is
recommended.
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.
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) msfit(cx,newdata,trans=tmat)#> $Haz #> time Haz trans #> 1 1 0.062046887 1 #> 2 5 0.062046887 1 #> 3 6 0.333333333 1 #> 4 7 0.333333333 1 #> 5 8 0.333333333 1 #> 6 9 1.333333333 1 #> 7 12 1.333333333 1 #> 8 1 0.007971491 2 #> 9 5 0.007971491 2 #> 10 6 0.007971491 2 #> 11 7 0.007971491 2 #> 12 8 0.305921143 2 #> 13 9 0.305921143 2 #> 14 12 0.305921143 2 #> 15 1 0.000000000 3 #> 16 5 1.000000000 3 #> 17 6 1.000000000 3 #> 18 7 1.500000000 3 #> 19 8 1.500000000 3 #> 20 9 2.500000000 3 #> 21 12 3.500000000 3 #> #> $varHaz #> time varHaz trans1 trans2 #> 1 1 0.0078708181 1 1 #> 2 5 0.0078708181 1 1 #> 3 6 0.1111111111 1 1 #> 4 7 0.1111111111 1 1 #> 5 8 0.1111111111 1 1 #> 6 9 1.1111111111 1 1 #> 7 12 1.1111111111 1 1 #> 8 1 0.0000000000 1 2 #> 9 5 0.0000000000 1 2 #> 10 6 0.0000000000 1 2 #> 11 7 0.0000000000 1 2 #> 12 8 0.0000000000 1 2 #> 13 9 0.0000000000 1 2 #> 14 12 0.0000000000 1 2 #> 15 1 0.0000000000 1 3 #> 16 5 0.0000000000 1 3 #> 17 6 0.0000000000 1 3 #> 18 7 0.0000000000 1 3 #> 19 8 0.0000000000 1 3 #> 20 9 0.0000000000 1 3 #> 21 12 0.0000000000 1 3 #> 22 1 0.0009825368 2 2 #> 23 5 0.0009825368 2 2 #> 24 6 0.0009825368 2 2 #> 25 7 0.0009825368 2 2 #> 26 8 0.1352337224 2 2 #> 27 9 0.1352337224 2 2 #> 28 12 0.1352337224 2 2 #> 29 1 0.0000000000 2 3 #> 30 5 0.0000000000 2 3 #> 31 6 0.0000000000 2 3 #> 32 7 0.0000000000 2 3 #> 33 8 0.0000000000 2 3 #> 34 9 0.0000000000 2 3 #> 35 12 0.0000000000 2 3 #> 36 1 0.0000000000 3 3 #> 37 5 1.0000000000 3 3 #> 38 6 1.0000000000 3 3 #> 39 7 1.2500000000 3 3 #> 40 8 1.2500000000 3 3 #> 41 9 2.2500000000 3 3 #> 42 12 3.2500000000 3 3 #> #> $trans #> to #> from healthy illness death #> healthy NA 1 2 #> illness NA NA 3 #> death NA NA NA #> #> attr(,"class") #> [1] "msfit"