loocv {frailtypack} | R Documentation |
The trials leave-one-out crossvalidation for the one-step Joint surrogate model for evaluating a canditate surrogate endpoint.
Description
The trials leave-one-out crossvalidation for evaluating the joint surrogate model
Usage
loocv(object, unusedtrial, var.used = "error.estim", alpha. = 0.05,
dec = 3, print.times = TRUE)
Arguments
object |
An object inheriting from |
unusedtrial |
A list of trial not to be taken into account in the cross-validation. This parameter is useful when after excluding some trials, the model is facing convergence problem. |
var.used |
This argument takes two values. The first one is |
alpha. |
The confidence level for the prediction interval. The default is |
dec |
The desired number of digits after the decimal point for parameters and confidence intervals. Default of 3 digits is used. |
print.times |
a logical parameter to print estimation time. Default is TRUE. |
Value
This function returns an object of class jointSurroPenalloocv
containing:
result |
A dataframe including for each trial the number of included subjects, the observed treatment effect on the surrogate endpoint, the observed treatment effect on the true endpoint and the predicted treatment effect on the true enpoint with the associated prediction intervals. If the observed treatment effect on the true endpoint is included into the prediction interval, the last columns contains "*". |
ntrial |
The number of trials in the meta-analysis |
notconvtrial |
The vector of trials that have not converged |
pred.error |
The prediction error, corresponding to the number of cases where the prediction interval does not included the observed treatment effect on T |
different.models |
The list of the |
loocv.summary |
A dataframe of the estimates for the |
Author(s)
Casimir Ledoux Sofeu casimir.sofeu@u-bordeaux.fr, scl.ledoux@gmail.com and Virginie Rondeau virginie.rondeau@inserm.fr
References
Burzykowski T, Buyse M (2006). "Surrogate threshold effect: an alternative measure for meta-analytic surrogate endpoint validation." Pharmaceutical Statistics, 5(3), 173-186.ISSN 1539-1612.
See Also
jointSurroPenal, jointSurroCopPenal
Examples
## Not run:
# Generation of data to use
data.sim <- jointSurrSimul(n.obs=300, n.trial = 10,cens.adm=549.24,
alpha = 1.5, theta = 3.5, gamma = 2.5, zeta = 1, sigma.s = 0.7,
sigma.t = 0.7, cor = 0.8, betas = -1.25, betat = -1.25,
full.data = 0, random.generator = 1, seed = 0,
nb.reject.data = 0)
###--- Joint surrogate model ---###
joint.surro.sim.MCGH <- jointSurroPenal(data = data.sim, int.method = 2,
nb.mc = 300, nb.gh = 20, print.iter = F)
# Example of loocv taking into accountn ony trial 2 trials (1 and 3)
dloocv <- loocv(joint.surro.sim.MCGH, unusedtrial = c(2,4:10))
dloocv$result
dloocv$loocv.summary
# In order to summarize all the estimated models during the loocv proccess:
dloocv$different.models
## End(Not run)