predictSurvProb {pec}R Documentation

Predicting survival probabilities

Description

Function to extract survival probability predictions from various modeling approaches. The most prominent one is the Cox regression model which can be fitted for example with `coxph' and with `cph'.

The function predictSurvProb is a generic function that means it invokes specifically designed functions depending on the 'class' of the first argument.

Usage

 predictSurvProb(object, newdata, times, ...)
## S3 method for class 'aalen':
predictSurvProb(object, newdata, times,...)
## S3 method for class 'cox.aalen':
predictSurvProb(object, newdata, times,...)
## S3 method for class 'coxph':
predictSurvProb(object, newdata, times,...)
## S3 method for class 'cph':
predictSurvProb(object, newdata, times,...)
## Default S3 method:
predictSurvProb(object, newdata, times,...)
## S3 method for class 'glm':
predictSurvProb(object, newdata, times,...)
## S3 method for class 'matrix':
predictSurvProb(object, newdata, times,...)
## S3 method for class 'mfp':
predictSurvProb(object, newdata, times,...)
## S3 method for class 'prodlim':
predictSurvProb(object, newdata, times,...)
## S3 method for class 'psm':
predictSurvProb(object, newdata, times,...)
## S3 method for class 'survfit':
predictSurvProb(object, newdata, times,...)
## S3 method for class 'phnnet':
predictSurvProb(object, newdata, times,train.data,...)
## S3 method for class 'survnnet':
predictSurvProb(object, newdata, times,train.data,...)
## S3 method for class 'rpart':
predictSurvProb(object, newdata, times,train.data,...)

Arguments

object A model for which predicted probabilities are desired.
newdata A data frame containing predictor variable combinations for which predictions are desired
times A vector of times in the range of the response variable, e.g. times when the response is a survival object, at which the exceedance probabilities (i.e. the survival probabilities) are returned.
train.data An optional data frame which contains the response and predictor variable combinations in which the prediction model was trained
... Additional arguments that are passed on to the current method.

Details

The function pec requires survival probabilities for each row in newdata at requested times. These probabilities are extracted from a fitted model of class CLASS with the function predictSurvProb.CLASS.

Currently there are predictSurvProb methods for objects of class cph (library Design), coxph (library survival), aalen (library timereg), cox.aalen (library timereg), mfp (library mfp), phnnet (library survnnet), survnnet (library survnnet), rpart (library rpart), product.limit (library prodlim), survfit (library survival), psm (library Design), glm (library stats).

Value

A matrix with as many rows as NROW(newdata) and as many columns as length(times). Each entry should be a probability and in rows the values should be decreasing.

Note

In order to assess the predictive performance of a new survival model a specific predictSurvProb S3 method has to be written. For examples, see the bodies of the existing methods.

The performance of the assessment procedure, in particular for resampling where the model is repeatedly evaluated, will be improved by supressing in the call to the model all the computations that are not needed for probability prediction. For example, se.fit=FALSE can be set in the call to cph.

Author(s)

Thomas A. Gerds tag@biostat.ku.dk

See Also

predict,survfit


[Package pec version 1.0.7 Index]