metrics {survivalPLANN} | R Documentation |
Metrics to Evaluate the Prognostic Capacities
Description
Compute several metrics to evaluate the prognostic capacities with time-to-event data.
Usage
metrics(formula, data, prediction.matrix, prediction.times, metric,
pro.time=NULL, ROC.precision=seq(.01, .99, by=.01))
Arguments
formula |
A formula object, with a survival object |
data |
A data frame for in which to look for the variables related to the status of the
follow-up time ( |
prediction.matrix |
A matrix or data.frame object of numeric values of dimension n*p, with n being the number of suject and p being the number of prognostic times. |
prediction.times |
A vector of numeric values with the times of the |
metric |
The metric to compute. See details. |
pro.time |
This optional value of prognostic time represents the maximum delay for which the capacity of the variable is evaluated. The same unit as the time variable in the formula. Not used for the following metrics: "loglik", "ibs", "bll", and "ibll". Default value is the time at which half of the subjects are still at risk. |
ROC.precision |
An optional argument with the percentiles (between 0 and 1) of the
prognostic variable used for computing each point of the time dependent ROC curve. Only used when
|
Details
The following metrics can be used: "bs" for the Brier score at the prognostic time pro.time
,
"ci" for the concordance index at the prognostic time pro.time
, "loglik" for the
log-likelihood, "ibs" for the integrated Brier score up to the last observed time of event, "ibll"
for the integrated binomial log-likelihood up to the last observed time of event, "bll" for the
binomial log-likelihood, "ribs" for the restricted integrated Brier score up to the prognostic time
pro.time
, "ribll" for the restricted integrated binomial log-likelihood up to the last
observed time of event, "bll" for the binomial log-likelihood, "auc" for the area under the
time-dependent ROC curve up to the prognostic time pro.time
.
Value
A numeric value with the metric estimation.
Examples
data(dataK)
# The estimation of the model
model <- sPLANN(formula = Surv(time, event)~stade, data = dataK, inter = 365.241)
#Predictions of the model
pred <- predict(model)
# The apparent AUC at 10-year post-transplantation
metrics(formula = Surv(time, event)~1, data=dataK,
prediction.matrix = pred$predictions, prediction.times=pred$times, metric
="auc", pro.time=10*365.241)
# The integrated Brier score up to 10 years post-transplanation
metrics(formula = Surv(time, event)~1, data=dataK,
prediction.matrix = pred$predictions, prediction.times=pred$times, metric
="ribs", pro.time=10*365.241)