| expand.covs.msdata {mstate} | R Documentation |
Given 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 class '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. |
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.
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).
Hein Putter H.Putter@lumc.nl
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.
# 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)
expand.covs(tglong,"x1")
expand.covs(tglong,"x1",longnames=FALSE)