
Calculate prediction error curves
pmpec.Rd
Calculation of prediction error curve from a survival response and predicted probabilities of survival.
Usage
pmpec(object, response=NULL, x=NULL, times, model.args=NULL,
type=c("PErr","NoInf"), external.time=NULL, external.status=NULL,
data=NULL)
Arguments
- object
fitted model of a class for which the interface function
predictProb.class
is available.- response
Either a survival object (with
Surv(time, status)
, where time is ann
-vector of censored survival times and status ann
-vector containing event status, coded with 0 and 1) or a matrix with columnstime
containing survival times andstatus
containing integers, where 0 indicates censoring, 1 the interesting event and larger numbers other competing risks.- x
n*p
matrix of covariates.- times
vector of time points at which the prediction error is to be estimated.
- model.args
named list of additional arguments, e.g. complexity value, which are to be passed to
predictProb
function.- type
type of output: Estimated prediction error (default) or no information error (prediction error obtained by permuting the data).
- external.time
optional vector of time points, used for censoring distribution.
- external.status
optional vector of status values, used for censoring distribution.
- data
Data frame containing
n
-vector of observed times ('time'),n
-vector of event status ('status') andn*p
matrix of covariates (remaining entries). Alternatively toresponse
andx
, for compatibility to pec.
Details
Prediction error of survival data is measured by the Brier score, which considers the squared difference of the true event status at a given time point and the predicted event status by a risk prediction model at that time. A prediction error curve is the weighted mean Brier score as a function of time at time points in times
(see References).
pmpec
requires a predictProb
method for the class of the fitted model, i.e. for a model of class class
predictProb.class
.
pmpec
is implemented to behave similar to function pec
of package pec, which provides several predictProb
methods.
In bootstrap framework, data
contains only a part of the full data set. For censoring distribution, the full data should be used to avoid extreme variance in case of small data sets. For that, the observed times and status values can be passed as argument external.time
and external.status
.
References
Gerds, A. and Schumacher, M. (2006) Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical Journal, 48, 1029–1040.
Schoop, R. (2008) Predictive accuracy of failure time models with longitudinal covariates. PhD thesis, University of Freiburg. http://www.freidok.uni-freiburg.de/volltexte/4995/.