loocv: Leave-one-trial-out cross-validation for treatment effect...

Description Usage Arguments Value Author(s) References Examples

View source: R/loocv.R

Description

The function loocv() computed leave-one-out 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 meta-analytic model fitted on the remaining trials (Michiels et al, 2009).

Usage

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## 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 surrosurv with an attribute data of class data.frame or a data.frame with columns

  • trialref, the trial reference

  • trt, the treatment arm (-0.5 or 0.5)

  • id, the patient id

  • timeT, the value of the true endpoint T

  • statusT, the censoring/event (0/1) indicator of the true endpoint T

  • timeS, the value of the surrogate endpoint S

  • statusS, the censoring/event (0/1) indicator of the surrogate endpoint S

nCores

The number of cores for parallel computing

parallel

Should results be computed using parallelization?

models, exact.models

Which models should be fitted (see surrosurv()). By default, the same models fitted in object (or x).

x

The fitted models, an object of class surrosurv

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 (classic) or the log-HR on the surrogate endpoint (regression).

main, ylab, xlab, ...

Further parameters to be passed to surrosurv (for loocv()) or to the generics print() and plot()

Value

An object of class loocvSurrosurv containing, for each trial:

margPars

the observed treatment effects on the surrogate ednpoint (alpha) and on the true endpoint (beta)

...

for each method in models the predicted value and prediction interval for beta.

Author(s)

Federico Rotolo [aut, cre], Xavier Paoletti [ctr], Marc Buyse [ctr], Tomasz Burzykowski [ctr], Stefan Michiels [ctr]

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

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  # Possibly long computation time!
  data('gastadv')
  cvRes <- loocv(gastadv)
  cvRes
  plot(cvRes)
  

surrosurv documentation built on Sept. 27, 2017, 9:04 a.m.