R: Expand covariates in multi-state dataset in long format

expand.covs.msdata {mstate} | R Documentation

## Expand covariates in multi-state dataset in long format

### Description

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.

### Usage

    ## S3 method for class 'msdata'
    expand.covs(data,covs,append=TRUE,longnames=TRUE,...)

### Arguments

`data` | An object of class `"msdata"`, such as output by `msprep`
`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
`...` | further arguments to be passed to or from other methods. They are ignored in this function.

### Details

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.

### Value

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`).

### Author(s)

Hein Putter H.Putter@lumc.nl

### References

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.

### Examples

    # 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)

[Package mstate version 0.2.12 Index]