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., $α_{i}$ ) and $T$ (i.e., $β_{i}$ ) in the different trials. In particular, $β_{i}$ is regressed on $α_{i}$ and the classical coefficient of determination of the fitted model provides an estimate of $R_{t r i a l}^{2}$ . 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., $α_{i}$ .

Beta.Vector

The vector of treatment effects on $T$ in the different trials, i.e., $β_{i}$ .

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 $α$ -level that is used to determine the confidence intervals around $R_{t r i a l}^{2}$ and $R_{t r i a l}$ . 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 ( $R_{t r i a l}^{2}$ ), its standard error and confidence interval.

Trial.R

A data.frame that contains the trial-level correlation coefficient ( $R_{t r i a l}$ ), 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.

Examples

Run this code

# 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)
# }

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