R: Predicting event probabilities from product limit estimates

predict.prodlim {prodlim} | R Documentation

## Predicting event probabilities from product limit estimates

### Description

Evaluation of estimated survival or event probabilities at given times and covariate constellations.

### Usage

    ## S3 method for class 'prodlim'
    predict(object, times, newdata, level.chaos = 1,
      type = c("surv", "cuminc", "list"), mode = "list", bytime = FALSE,
      cause, ...)

### Arguments

`object` | A fitted object of class "prodlim".
`times` | Vector of times at which to return the estimated probabilities (survival or absolute event risks).
`newdata` | A data frame with the same variable names as those that appear on the right hand side of the 'prodlim' formula. If there are covariates this argument is required.
`level.chaos` | Integer specifying the sorting of the output: ‘0’ sort by time and newdata; ‘1’ only by time; ‘2’ no sorting at all
`type` | Choice between "surv","cuminc","list": "surv": predict survival probabilities only survival models "cuminc": predict cumulative incidences only competing risk models "list": find the indices corresponding to times and newdata. See value. Defaults to "surv" for two-state models and to "cuminc" for competing risk models.
`mode` | Only for `type=="surv"` and `type=="cuminc"`. Can either be "list" or "matrix". For "matrix" the predicted probabilities will be returned in matrix form.
`bytime` | Logical. If TRUE and `mode=="matrix"` the matrix with predicted probabilities will have a column for each time and a row for each newdata. Only when `object$covariate.type>1` and more than one time is given.
`cause` | Character (other classes are converted with `as.character`). The cause for predicting the cause-specific cumulative incidence function in competing risk models, that is the absolute risk of `cause` events between time zero and `times` .
`...` | Only for compatibility reasons.

### Details

Predicted (survival) probabilities are returned that can be plotted, summarized and used for inverse of probability of censoring weighting.

### Value

`type=="surv"` A list or a matrix with survival probabilities for all times and all newdata.

`type=="cuminc"` A list or a matrix with cumulative incidences for all times and all newdata.

`type=="list"` A list with the following components:

`times` | The argument `times` carried forward
`predictors` | The relevant part of the argument `newdata`.
`indices` | A list with the following components `time`: Where to find values corresponding to the requested times `strata`: Where to find values corresponding to the values of the variables in newdata. Together time and strata show where to find the predicted probabilities.
`dimensions` | a list with the following components: `time` : The length of `times` `strata` : The number of rows in `newdata` `names.strata` : Labels for the covariate values.

### Author(s)

Thomas Alexander Gerds <tag@biostat.ku.dk>

### See Also

`predictSurvIndividual`

### Examples

    dat <- SimSurv(400)
    fit <- prodlim(Hist(time,status)~1,data=dat)

    ## predict the survival probs at selected times
    predict(fit,times=c(3,5,10))

    ## NA is returned when the time point is beyond the
    ## range of definition of the Kaplan-Meier estimator:
    predict(fit,times=c(-1,0,10,100,1000,10000))

    ## when there are strata, newdata is required
    ## or neighborhoods (i.e. overlapping strata)
    mfit <- prodlim(Hist(time,status)~X1+X2,data=dat)
    predict(mfit,times=c(-1,0,10,100,1000,10000),newdata=dat[18:21,])

    ## this can be requested in matrix form
    predict(mfit,times=c(-1,0,10,100,1000,10000),newdata=dat[18:21,],mode="matrix")

    ## and even transposed
    predict(mfit,times=c(-1,0,10,100,1000,10000),newdata=dat[18:21,],mode="matrix",bytime=TRUE)

[Package prodlim version 2018.04.18 Index]