expand.covs {mstate} | R Documentation |
Given a competing risks dataset in stacked format, and one or more covariates, this function adds type-specific covariates to the dataset. The original dataset with the type-specific covariates appended is returned.
## Default S3 method: expand.covs(data, covs, append=TRUE, longnames=FALSE, event.types="failcode",...)
data |
A data frame in stacked format for competing risks analyses, such as generated by
|
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 categorical expanded
covariates. In case of |
event.types |
Character value with the name of the column that contains the event type information. |
... |
Further arguments to be passed to or from other methods. They are ignored in this function. |
Type-specific covariates can be used to analyse separate
effects on all event types in a single analysis based on a stacked
data set (Putter, Fiocco & Geskus (2007) and Geskus (2016)). It is
only unambiguously defined for numeric covariates or for explicit
codings. Rows that contain the data for that specific event type
have the value copied from the original covariate in case it is
numeric. In all other rows it has the value zero. If the covariate is
a factor, it will be expanded on the design matrix given by
model.matrix
. For standard
"treatment contrasts" this means that dummy variables are created.
If the covariate is a factor, the column
name combines the name of the covariate with the specific event
type. If longnames
=TRUE
, both parts are intersected by
the specific labels in the coding. Missing values in the basic
covariates are allowed and result in missing values in the expanded
covariates.
An data frame object of the same class as the data argument,
containing the design matrix for the type-specific covariates,
either on its own (append
=FALSE
) or appended to the data
(append
=TRUE
).
Ronald Geskus and Hein Putter H.Putter@lumc.nl
Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 26, 2389–2430.
Geskus, Ronald B. (2016). Data Analysis with Competing Risks and Intermediate States. CRC Press, Boca Raton.
# small data set in stacked format tg <- data.frame(time=c(5,5,1,1,9,9),status=c(1,0,2,2,0,1),failcode=rep(c("I","II"),3), x1=c(1,1,2,2,2,2),x2=c(3,3,2,2,1,1)) tg$x1 <- factor(tg$x1,labels=c("male","female")) # expanded covariates expand.covs(tg,covs=c("x1","x2")) expand.covs(tg,covs=c("x1","x2"),longnames=TRUE) expand.covs(tg,covs=c("x1","x2"),append=FALSE)