loocv: The trials leave-one-out crossvalidation for the one-step...

In frailtypack: Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

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 jointSurroPenal class (output from calling the function jointSurroPenal or jointSurroCopPenal).
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 "error.estim" and indicates if the prediction variance takes into account the estimation errors from the estimates of the parameters. If estimates are supposed to be known or if the dataset includes a high number of trials with a high number of subject per trial, value "No.error" can be used. The default is error.estim.
alpha.	The confidence level for the prediction interval. The default is 0.05
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 G models obtained after excuded for the i-th trial
loocv.summary	A dataframe of the estimates for the G models; each raw including the results without the subjects of the given trial

Author(s)

Casimir Ledoux Sofeu [e-mail], [e-mail] and Virginie Rondeau [e-mail]

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)

frailtypack documentation built on Oct. 20, 2024, 1:08 a.m.