Description Usage Arguments Value Author(s) References Examples
The function loocv()
computed leaveoneout predcition 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 metaanalytic model fitted on the remaining trials
(Michiels et al, 2009).
1 2 3 4 5 6 7 8 9 10  ## 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, ...)

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 xscale for the loocv plot: either the trial number ( 
main, ylab, xlab, ... 
Further parameters to be passed to 
An object of class loocvSurrosurv
containing, for each trial:
margPars 
the observed treatment effects
on the surrogate ednpoint ( 
... 
for each method in 
Federico Rotolo [aut], Xavier Paoletti [ctr], Marc Buyse [ctr], Tomasz Burzykowski [ctr], Stefan Michiels [ctr, cre]
Michiels S, Le Maitre A, Buyse M, et al. Surrogate endpoints for overall survival in locally advanced head and neck cancer: metaanalyses of individual patient data. Lancet Oncol. 2009;10(4):34150. doi:10.1016/S14702045(09)700233
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