loocv {surrosurv} | R Documentation |
Leave-one-trial-out cross-validation for treatment effect prediction
Description
The function loocv()
computed leave-one-out prediction of the treatment
effect on the true endpoint for each trial,
based on the observed effect on the surrogate endpoint in the trial itself
and based on the meta-analytic model fitted on the remaining trials
(Michiels et al, 2009).
Usage
## S3 method for class 'surrosurv'
loocv(object, models, nCores, parallel = TRUE, ...)
## S3 method for class 'loocvSurrosurv'
print(x, n = min(length(x), 6), silent = FALSE, ...)
## S3 method for class 'loocvSurrosurv'
plot(x, models, exact.models,
plot.type = c('classic', 'regression'),
main, ylab, xlab, ...)
Arguments
object |
Either an object of class
|
nCores |
The number of cores for parallel computing |
parallel |
Should results be computed using parallelization? |
models , exact.models |
Which models should be fitted (see |
x |
The fitted models, an object of class |
n |
the number of rows to print |
silent |
Should the results be return for storing without printing them? |
plot.type |
The type ox x-scale for the loocv plot: either the trial number ( |
main , ylab , xlab , ... |
Further parameters to be passed to |
Value
An object of class loocvSurrosurv
containing, for each trial:
margPars |
the observed treatment effects
on the surrogate ednpoint ( |
... |
for each method in |
Author(s)
NA
References
Michiels S, Le Maitre A, Buyse M, et al. Surrogate endpoints for overall survival in locally advanced head and neck cancer: meta-analyses of individual patient data. Lancet Oncol. 2009;10(4):341-50. doi: 10.1016/S1470-2045(09)70023-3
Examples
## Not run:
# Possibly long computation time!
data('gastadv')
cvRes <- loocv(gastadv)
cvRes
plot(cvRes)
## End(Not run)