SPP.BinCont: Evaluate the surrogate predictive function (SPF) in the...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Evaluate the surrogate predictive function (SPF) in the binary-continuous setting (sensitivity-analysis based approach)

Description

Computes the surrogate predictive function (SPF) based on sensitivity-analyis, i.e., P(\Delta T | \Delta S \in I[ab]), in the setting where S is continuous and T is a binary endpoint.

Usage

SPF.BinCont(x, a, b)

Arguments

x	A fitted object of class ICA.BinCont.
a	The lower interval a in P(\Delta T | \Delta S \in I[ab]).
b	The upper interval b in P(\Delta T | \Delta S \in I[ab]).

Value

a	The lower interval a in P(\Delta T | \Delta S \in I[ab]).
b	The upper interval b in P(\Delta T | \Delta S \in I[ab]).
P_Delta_T_min1	The vector of values for P(\Delta T = -1| \Delta S \in I[ab]).
P_Delta_T_0	The vector of values for P(\Delta T = 0| \Delta S \in I[ab]).
P_Delta_T_1	The vector of values for P(\Delta T = 1| \Delta S \in I[ab]).

Author(s)

Wim Van der Elst & Ariel Alonso

References

Alonso, A., Van der Elst, W., Molenberghs, G., & Verbeke, G. (2017). Assessing the predictive value of a continuous surogate for a binary true endpoint based on causal inference.

See Also

ICA.BinBin, plot.SPF.BinCont

Examples

## Not run:  # time consuming code part
# Use ICA.BinCont to examine surrogacy
data(Schizo_BinCont)
Result_BinCont <- ICA.BinCont(M = 1000, Dataset = Schizo_BinCont,
Surr = PANSS, True = CGI_Bin, Treat=Treat, Diff.Sigma=TRUE)

# Obtain SPF
Fit <- SPF.BinCont(x=Result_BinCont, a = -30, b = -3)

# examine results
summary(Fit1)
plot(Fit1)

## End(Not run)

Surrogate documentation built on Sept. 25, 2023, 5:07 p.m.