Surrogate (version 3.3.1)

SPF.BinCont: Evaluate the surrogate predictive function (SPF) in the causal-inference single-trial setting in the binary-continuous case

Description

The function SPF.BinCont computes the surrogate predictive function (SPF), i.e., the \(P[\Delta T | \Delta S \in I_{ab}]\) in the single-trial setting within the causal-inference framework when the surrogate endpoint is continuous (normally distributed) and the true endpoint is a binary outcome. For details, see Alonso et al. (2024).

Usage

SPF.BinCont(x, a, b)

Value

An object of class SPF.BinCont with important or relevant components:

a

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

b

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

r_min1_min1

The vector of \(P[\Delta T = -1 | \Delta S \in I_{(-\infty,a)}]\).

r_0_min1

The vector of \(P[\Delta T = 0 | \Delta S \in I_{(-\infty,a)}]\).

r_1_min1

The vector of \(P[\Delta T = 1 | \Delta S \in I_{(-\infty,a)}]\).

r_min1_0

The vector of \(P[\Delta T = -1 | \Delta S \in I_{(a,b)}]\).

r_0_0

The vector of \(P[\Delta T = 0 | \Delta S \in I_{(a,b)}]\).

r_1_0

The vector of \(P[\Delta T = 1 | \Delta S \in I_{(a,b)}]\).

r_min1_1

The vector of \(P[\Delta T = -1 | \Delta S \in I_{(b,\infty)}]\).

r_0_1

The vector of \(P[\Delta T = 0 | \Delta S \in I_{(b,\infty)}]\).

r_1_1

The vector of \(P[\Delta T = 1 | \Delta S \in I_{(b,\infty)}]\).

P_DT_0_DS_0

The vector of \(P[\Delta T = 0 | \Delta S = 0]\).

P_DT_psi_DS_max

The vector of \(P[\Delta T = \tilde{\psi}_{ab}(\Delta S)]\), where \(\tilde{\psi}_{ab}(\Delta S)=arg max_{i}P[\Delta T=i|\Delta S \in (x,y)]\).

best.pred.min1

The vector of \(\tilde{\psi}_{ab}(\Delta S)=arg max_{i}P[\Delta T=i|\Delta S \in (x,y)]\), where \((x,y)=(-\infty,a)\).

best.pred.0

The vector of \(\tilde{\psi}_{ab}(\Delta S)=arg max_{i}P[\Delta T=i|\Delta S \in (x,y)]\), where \((x,y)=(a,b)\).

best.pred.1

The vector of \(\tilde{\psi}_{ab}(\Delta S)=arg max_{i}P[\Delta T=i|\Delta S \in (x,y)]\), where \((x,y)=(b,\infty)\).

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 \(P[\Delta T | \Delta S \in I_{ab}]\).

Author

Fenny Ong, Wim Van der Elst, Ariel Alonso, and Geert Molenberghs

References

Alonso, A., Ong, F., Van der Elst, W., Molenberghs, G., & Callegaro, A. (2024). Assessing a continuous surrogate predictive value for a binary true endpoint based on causal inference and information theory in vaccine trial.

See Also

ICA.BinCont, ICA.BinCont.BS, plot.SPF.BinCont

Examples

if (FALSE) # Time consuming code part
data(Schizo)
fit.ica <- ICA.BinCont.BS(Dataset = Schizo, Surr = BPRS, True = PANSS_Bin, nb = 10,
Theta.S_0=c(-10,-5,5,10,10,10,10,10), Theta.S_1=c(-10,-5,5,10,10,10,10,10),
Treat=Treat, M=50, Seed=1)

fit.spf <- SPF.BinCont(fit.ica, a=-5, b=5)

summary(fit.spf)
plot(fit.spf)