R/expand.covs.R
expand.covs.msdata.RdGiven a multi-state dataset in long format, and one or more covariates, this function adds transition-specific covariates, expanding the original covariate(s), to the dataset. The original dataset with the transition-specific covariates appended is returned.
# S3 method for msdata expand.covs(data, covs, append = TRUE, longnames = TRUE, ...)
| data | An object of class |
|---|---|
| covs | A character vector containing the names of the covariates in
|
| append | Logical value indicating whether or not the design matrix for
the expanded covariates should be appended to the data (default= |
| longnames | Logical value indicating whether or not the labels are to
be used for the names of the expanded covariates that are categorical
(default= |
| ... | Further arguments to be passed to or from other methods. They are ignored in this function. |
An object of class 'msdata', containing the design matrix for the
transition- specific covariates, either on its own
(append=FALSE) or appended to the data
(append=TRUE).
For a given basic covariate Z, the transition-specific covariate for
transition s is called Z.s. The concept of transition-specific
covariates in the context of multi-state models was introduced by Andersen,
Hansen & Keiding (1991), see also Putter, Fiocco & Geskus (2007). It is only
unambiguously defined for numeric covariates or for explicit codings. Then
it will take the value 0 for all rows in the long format dataframe for which
trans does not equal s. For the rows for which trans
equals s, the original value of Z is copied. In
expand.covs, when a given covariate is a factor, it will be expanded
on the design matrix given by
model.matrix. Missing values in the basic
covariates are allowed and result in missing values in the expanded
covariates.
Andersen PK, Hansen LS, Keiding N (1991). Non- and semi-parametric estimation of transition probabilities from censored observation of a non-homogeneous Markov process. Scandinavian Journal of Statistics 18, 153--167.
Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 26, 2389--2430.
Hein Putter H.Putter@lumc.nl
# transition matrix for illness-death model tmat <- trans.illdeath() # small data set in wide format 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(1,1,1,2,2,2),x2=c(6:1)) tg$x1 <- factor(tg$x1,labels=c("male","female")) # 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) # expanded covariates expand.covs(tglong,c("x1","x2"),append=FALSE)#> x1female.1 x1female.2 x1female.3 x2.1 x2.2 x2.3 #> 1 0 0 0 6 0 0 #> 2 0 0 0 0 6 0 #> 3 0 0 0 0 0 6 #> 4 0 0 0 5 0 0 #> 5 0 0 0 0 5 0 #> 6 0 0 0 4 0 0 #> 7 0 0 0 0 4 0 #> 8 0 0 0 0 0 4 #> 9 1 0 0 3 0 0 #> 10 0 1 0 0 3 0 #> 11 0 0 1 0 0 3 #> 12 1 0 0 2 0 0 #> 13 0 1 0 0 2 0 #> 14 1 0 0 1 0 0 #> 15 0 1 0 0 1 0 #> 16 0 0 1 0 0 1#> An object of class 'msdata' #> #> Data: #> id from to trans Tstart Tstop time status x1 x2 x1female.1 x1female.2 #> 1 1 1 2 1 0 1 1 1 male 6 0 0 #> 2 1 1 3 2 0 1 1 0 male 6 0 0 #> 3 1 2 3 3 1 5 4 1 male 6 0 0 #> 4 2 1 2 1 0 1 1 0 male 5 0 0 #> 5 2 1 3 2 0 1 1 1 male 5 0 0 #> 6 3 1 2 1 0 6 6 1 male 4 0 0 #> 7 3 1 3 2 0 6 6 0 male 4 0 0 #> 8 3 2 3 3 6 9 3 1 male 4 0 0 #> 9 4 1 2 1 0 6 6 1 female 3 1 0 #> 10 4 1 3 2 0 6 6 0 female 3 0 1 #> 11 4 2 3 3 6 7 1 1 female 3 0 0 #> 12 5 1 2 1 0 8 8 0 female 2 1 0 #> 13 5 1 3 2 0 8 8 1 female 2 0 1 #> 14 6 1 2 1 0 9 9 1 female 1 1 0 #> 15 6 1 3 2 0 9 9 0 female 1 0 1 #> 16 6 2 3 3 9 12 3 1 female 1 0 0 #> x1female.3 #> 1 0 #> 2 0 #> 3 0 #> 4 0 #> 5 0 #> 6 0 #> 7 0 #> 8 0 #> 9 0 #> 10 0 #> 11 1 #> 12 0 #> 13 0 #> 14 0 #> 15 0 #> 16 1#> An object of class 'msdata' #> #> Data: #> id from to trans Tstart Tstop time status x1 x2 x1.1 x1.2 x1.3 #> 1 1 1 2 1 0 1 1 1 male 6 0 0 0 #> 2 1 1 3 2 0 1 1 0 male 6 0 0 0 #> 3 1 2 3 3 1 5 4 1 male 6 0 0 0 #> 4 2 1 2 1 0 1 1 0 male 5 0 0 0 #> 5 2 1 3 2 0 1 1 1 male 5 0 0 0 #> 6 3 1 2 1 0 6 6 1 male 4 0 0 0 #> 7 3 1 3 2 0 6 6 0 male 4 0 0 0 #> 8 3 2 3 3 6 9 3 1 male 4 0 0 0 #> 9 4 1 2 1 0 6 6 1 female 3 1 0 0 #> 10 4 1 3 2 0 6 6 0 female 3 0 1 0 #> 11 4 2 3 3 6 7 1 1 female 3 0 0 1 #> 12 5 1 2 1 0 8 8 0 female 2 1 0 0 #> 13 5 1 3 2 0 8 8 1 female 2 0 1 0 #> 14 6 1 2 1 0 9 9 1 female 1 1 0 0 #> 15 6 1 3 2 0 9 9 0 female 1 0 1 0 #> 16 6 2 3 3 9 12 3 1 female 1 0 0 1