The function ICA.BinCont
quantifies surrogacy in the single-trial causal-inference framework (individual causal association) when the surrogate endpoint is continuous (normally distributed) and the true endpoint is a binary outcome. For details, see Alonso et al. (2016).
ICA.BinCont(Dataset, Surr, True, Treat, Diff.Sigma=FALSE,
G_pi_00=seq(0, 1, by=.01), G_rho_01_00=seq(-1, 1, by=.01),
G_rho_01_01=seq(-1, 1, by=.01), G_rho_01_10=seq(-1, 1, by=.01),
G_rho_01_11=seq(-1, 1, by=.01), M=1000, Seed=123,
Plots=TRUE, Save.Plots="No", Test.Fit.Mixture=FALSE,
Test.Fit.Mixture.Alpha=0.01, Test.Fit.Details=FALSE,
Keep.All=FALSE)
A data.frame
that should consist of one line per patient. Each line contains (at least) a surrogate value, a true endpoint value, and a treatment indicator.
The name of the variable in Dataset
that contains the surrogate endpoint values.
The name of the variable in Dataset
that contains the true endpoint values.
The name of the variable in Dataset
that contains the treatment indicators. The treatment indicator should be coded as
Logical. If Diff.Sigma=TRUE
, then the mixtures of normal distributions are allowed to have different variances. If Diff.Sigma=FALSE
, then the mixtures of normal distributions are not allowed to have different variances (selecting the latter option speeds up the required calculations). Default Diff.Sigma=FALSE
.
A grid of values to be considered for seq(0, 1, by=.01)
.
A grid of values to be considered for the association parameter seq(-1, 1, by=.01)
.
A grid of values to be considered for the association parameter seq(-1, 1, by=.01)
.
A grid of values to be considered for the association parameter seq(-1, 1, by=.01)
.
A grid of values to be considered for the association parameter seq(-1, 1, by=.01)
.
The number of valid ICA values to be sampled. Default M=1000
.
The seed to be used to generate Seed=123
.
Logical. Should histograms of Plots=TRUE
.
Should the plots (see previous item) be saved? If Save.Plots="No"
, no plots are saved. If plots have to be saved, replace "No"
by the desired location, e.g., Save.Plots="C:/"
. Default Save.Plots="No"
.
Should the fit of the densities of the mixture distributions with the observed densities of the surrogates in the control and experimental treatment groups be conducted? For details on the method used, see Wilcox (1995, 2014). The code used to conduct the analysis is provided by Wilcox, see http://dornsife.usc.edu/labs/rwilcox/software/. Default Test.Fit.Mixture=FALSE
.
The Test.Fit.Mixture.Alpha=0.01
.
Should the details of the Wilcox-testing procedure be saved? Default Test.Fit.Details=FALSE
When Test.Fit.Mixture
is used, the Wilcox-testing procedure is used to evaluate model fit and only models for which the fit is OK (i.e., all p-values above the specified Keep.All=TRUE
can be used. Default Keep.All=FALSE
.
An object of class ICA.BinCont
with components,
The vector of the
The vector of
The vector of
The vector of
The vector of
The vector of the
The vector of the
The vector of the
The vector of the
The vector of the
The vector of the
The vector of the
The vector of
The vector of
The vector of
The vector of
The vector of
The vector of
The vector of
The vector of
The vector of squared
The vector of squared
The vector of squared
The vector of squared
The vector of
The vector of
The vector of
The vector of
The vector of
The vector of
The vector of
The vector of
The vector of squared
The vector of squared
The vector of squared
The vector of squared
Is the fit of the mixture distribution for
Is the fit of the mixture distribution for
Details of the Wilcox-testing procedure. This information is provided when the argument Test.Fit.Details=FALSE
was used in the function call.
Alonso, A., Van der Elst, W., & Molenberghs, G. (2016). Surrogate markers validation: the continuous-binary setting from a causal inference perspective.
Wilcox, R.R. (1995) Comparing Two Independent Groups Via Multiple Quantiles. Journal of the Royal Statistical Society. Series D (The Statistician), 44, 91-99.
Wilcox, R. R., Erceg-Hurn, D. M, Clark, F., & Carlson, M. (2014). Comparing two independent groups via the lower and upper quantiles. Journal of Statistical Computation and Simulation.
# NOT RUN {
# Time consuming code part
data(Schizo)
Fit <- ICA.BinCont(Dataset = Schizo, Surr = BPRS, True = PANSS_Bin,
Treat=Treat, M=50, Seed=1)
summary(Fit)
plot(Fit)
# }
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