frailtypack (version 3.4.0)

print.jointSurroPenal: Summary of the random effects parameters, the fixed treatment effects, and the surrogacy evaluation criteria estimated from a joint surrogate model

Description

This function returns the estimate of the coefficients and their standard error with p-values of the Wald test for the joint surrogate model, also hazard ratios (HR) and their confidence intervals for the fixed treatment effects, and finaly an estimate of the surrogacy evaluation criterian (Kendall's \(\tau\) and R)

Usage

# S3 method for jointSurroPenal
print(x, d = 4, len = 3, nb.gh = 32, ...)

Arguments

x

An object inheriting from jointSurroPenal class.

d

The desired number of digits after the decimal point for parameters. The maximum of 4 digits is required for the estimates. Default of 3 digits is used.

len

The desired number of digits after the decimal point for p-value and convergence criteria. Default of 4 digits is used.

nb.gh

Number of nodes for the Gaussian-Hermite quadrature. The default is 32 1 for Gaussian-Hermite quadrature.

…

other unused arguments.

Value

For the variances parameters of the random effects, it prints the estimate of the coefficients with their standard error, Z-statistics and p-values of the Wald test. For the fixed treatment effects, it also prints HR and its confidence intervals for each covariate. For the surrogacy evaluation criteria, its prints the estimated Kendall's \(\tau\) with its 95% Confidence interval obtained by the parametric bootstrap or Delta-method, the estimated R(R2trial) with standard error and the 95% Confidence interval obtained by Delta-method (Dowd et al., 2014), R(R2.boot) and its 95% Confidence interval obtained by the parametric bootstrap. We notice that, using bootstrap, the standard error of the point estimate is not available. We propose a classification of R according to the suggestion of the Institute of Quality and Efficiency in Health Care (Prasad et al., 2015). We also display the surrogate threshold effect (ste) with the associated hazard risk. The rest of parameters concerns the convergence characteristics and included: the penalized marginal log-likelihood, the number of iterations, the LCV and the Convergence criteria.

References

Dowd BE, Greene WH, Norton EC (2014). "Computation of Standard Errors." Health Services Research, 49(2), 731-750.

Prasad V, Kim C, Burotto M, Vandross A (2015). "The strength of association between surrogate end points and survival in oncology: A systematic review of trial-level meta- alyses." JAMA Internal Medicine, 175(8), 1389-1398.

See Also

jointSurroPenal, jointSurroCopPenal, jointSurroTKendall

Examples

# NOT RUN {

# }
# NOT RUN {
###---Data generation---###
data.sim <-jointSurrSimul(n.obs=400, n.trial = 20,cens.adm=549, 
          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)

###---Estimation---###
joint.surrogate <- jointSurroPenal(data = data.sim, nb.mc = 300, 
                   nb.gh = 20, indicator.alpha = 1, n.knots = 6)
                            
print(joint.surrogate)

# or
joint.surrogate
# }
# NOT RUN {

# }