SPF.BinCont: 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

A fitted object of class ICA.BinCont.

The lower interval \(a\) in \(P(\Delta T | \Delta S \in I[ab])\).

The upper interval \(b\) in \(P(\Delta T | \Delta S \in I[ab])\).

Value

The lower interval \(a\) in \(P(\Delta T | \Delta S \in I[ab])\).

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 \(P(\Delta T = 1| \Delta S \in I[ab])\).

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.

Examples

Run this code

# 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)
# }