SPF.BinCont {Surrogate}

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)

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

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).

Author(s)

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

## Not run: # 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)

## End(Not run)