Surrogate (version 1.7)

TrialLevelMA: Estimates trial-level surrogacy in the meta-analytic framework

Description

The function TrialLevelMA estimates trial-level surrogacy based on the vectors of treatment effects on \(S\) (i.e., \(\alpha_{i}\)) and \(T\) (i.e., \(\beta_{i}\)) in the different trials. In particular, \(\beta_{i}\) is regressed on \(\alpha_{i}\) and the classical coefficient of determination of the fitted model provides an estimate of \(R^2_{trial}\). In addition, the standard error and CI are provided.

Usage

TrialLevelMA(Alpha.Vector, Beta.Vector, 
N.Vector, Weighted=TRUE, Alpha=.05)

Arguments

Alpha.Vector

The vector of treatment effects on \(S\) in the different trials, i.e., \(\alpha_{i}\).

Beta.Vector

The vector of treatment effects on \(T\) in the different trials, i.e., \(\beta_{i}\).

N.Vector

The vector of trial sizes \(N_{i}\).

Weighted

Logical. If TRUE, then a weighted regression analysis is conducted. If FALSE, then an unweighted regression analysis is conducted. Default TRUE.

Alpha

The \(\alpha\)-level that is used to determine the confidence intervals around \(R^2_{trial}\) and \(R_{trial}\). Default \(0.05\).

Value

An object of class TrialLevelMA with components,

Alpha.Vector

The vector of treatment effects on \(S\) in the different trials.

Beta.Vector

The vector of treatment effects on \(T\) in the different trials.

N.Vector

The vector of trial sizes \(N_{i}\).

Trial.R2

A data.frame that contains the trial-level coefficient of determination (\(R^2_{trial}\)), its standard error and confidence interval.

Trial.R

A data.frame that contains the trial-level correlation coefficient (\(R_{trial}\)), its standard error and confidence interval.

Model.2.Fit

The fitted stage \(2\) model.

References

Burzykowski, T., Molenberghs, G., & Buyse, M. (2005). The evaluation of surrogate endpoints. New York: Springer-Verlag.

Buyse, M., Molenberghs, G., Burzykowski, T., Renard, D., & Geys, H. (2000). The validation of surrogate endpoints in meta-analysis of randomized experiments. Biostatistics, 1, 49-67.

See Also

UnimixedContCont, UnifixedContCont, BifixedContCont, BimixedContCont, plot Meta-Analytic

Examples

# NOT RUN {
# Generate vector treatment effects on S
set.seed(seed = 1)
Alpha.Vector <- seq(from = 5, to = 10, by=.1) + runif(min = -.5, max = .5, n = 51)
# Generate vector treatment effects on T
set.seed(seed=2)
Beta.Vector <- (Alpha.Vector * 3) + runif(min = -5, max = 5, n = 51)
# Vector of sample sizes of the trials (here, all n_i=10)
N.Vector <- rep(10, times=51)

# Apply the function to estimate R^2_{trial}
Fit <- TrialLevelMA(Alpha.Vector=Alpha.Vector,
Beta.Vector=Beta.Vector, N.Vector=N.Vector)

# Plot the results and obtain summary
plot(Fit)
summary(Fit)
# }