Pred.TrialT.ContCont {Surrogate} | R Documentation |
Compute the expected treatment effect on the true endpoint in a new trial (when both S and T are normally distributed continuous endpoints)
Description
The key motivation to evaluate a surrogate endpoint is to be able to predict the treatment effect on the true endpoint based on the treatment effect on
in a new trial
. The function
Pred.TrialT.ContCont
allows for making such predictions based on fitted models of class BimixedContCont
, BifixedContCont
, UnimixedContCont
and UnifixedContCont
.
Usage
Pred.TrialT.ContCont(Object, mu_S0, alpha_0, alpha.CI=0.05)
Arguments
Object |
A fitted object of class |
mu_S0 |
The intercept of a regression model in the new trial |
alpha_0 |
The regression weight of the treatment in the regression model specified under argument |
alpha.CI |
The |
Details
The key motivation to evaluate a surrogate endpoint is to be able to predict the treatment effect on the true endpoint based on the treatment effect on
in a new trial
.
When a so-called full (fixed or mixed) bi- or univariate model was fitted in the surrogate evaluation phase (for details, see BimixedContCont
, BifixedContCont
, UnimixedContCont
and UnifixedContCont
), this prediction is made as:
where all components are defined as in BimixedContCont
. When the univariate mixed-effects models are used or the (univariate or bivariate) fixed effects models, the fitted components contained in D.Equiv
are used instead of those in D
.
When a reduced-model approach was used in the surrogate evaluation phase, the prediction is made as:
where all components are defined as in BimixedContCont
. When the univariate mixed-effects models are used or the (univariate or bivariate) fixed effects models, the fitted components contained in D.Equiv
are used instead of those in D
.
A prediction interval for
can be obtained as
(and similarly for
).
Value
Beta_0 |
The predicted |
Variance |
The variance of the prediction. |
Lower |
The lower bound of the confidence interval around the expected |
Upper |
The upper bound of the confidence interval around the expected |
alpha.CI |
The |
Surr.Model |
The model that was used to compute |
alpha_0 |
The slope of the regression model specified in the |
Author(s)
Wim Van der Elst, Ariel Alonso, & Geert Molenberghs
References
Burzykowski, T., Molenberghs, G., & Buyse, M. (2005). The evaluation of surrogate endpoints. New York: Springer-Verlag.
See Also
UnifixedContCont
, BifixedContCont
, UnimixedContCont
Examples
## Not run: #time-consuming code parts
# Generate dataset
Sim.Data.MTS(N.Total=2000, N.Trial=15, R.Trial.Target=.8,
R.Indiv.Target=.8, D.aa=10, D.bb=50, Fixed.Effects=c(1, 2, 30, 90),
Seed=1)
# Evaluate surrogacy using a reduced bivariate mixed-effects model
BimixedFit <- BimixedContCont(Dataset = Data.Observed.MTS, Surr = Surr,
True = True, Treat = Treat, Trial.ID = Trial.ID, Pat.ID = Pat.ID,
Model="Reduced")
# Suppose that in a new trial, it was estimated alpha_0 = 30
# predict beta_0 in this trial
Pred_Beta <- Pred.TrialT.ContCont(Object = BimixedFit,
alpha_0 = 30)
# Examine the results
summary(Pred_Beta)
# Plot the results
plot(Pred_Beta)
## End(Not run)