| expand.covs {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.
expand.covs(data,trans,covs,append=TRUE,longnames=TRUE)
data |
Data frame in long format, such as output by
msprep |
trans |
Transition matrix describing the states and
transitions in the multi-state model. See trans in
msprep for more detailed information |
covs |
A character vector containing the names of
the covariates in data to be expanded |
append |
Logical value indicating whether or not the design
matrix for the expanded covariates should be appended to the data
(default=TRUE) |
longnames |
Logical value indicating whether or not the labels
are to be used for the names of the expanded covariates that are
categorical (default=TRUE); in case of FALSE numbers
from 1 up to the number of contrasts are used |
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.
A data frame 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,tmat,c("x1","x2"),append=FALSE)
expand.covs(tglong,tmat,"x1")
expand.covs(tglong,tmat,"x1",longnames=FALSE)