SurrogateTest (version 1.3)

var.delta.eb: Variance estimation

Description

Variance estimation, generally not called directly by the user

Usage

var.delta.eb(Axzero, Adeltazero, Aszero, Bxone, Bdeltaone, 
Bsone, Bxzero, Bdeltazero, Bszero, t, landmark = landmark, 
extrapolate)

Arguments

Axzero

observed event times in the control group in Study A

Adeltazero

event/censoring indicators in the control group in Study A

Aszero

surrogate marker values in the control group in Study A, NA for individuals not observable at the time the surrogate marker was measured

Bxone

observed event times in the treatment group in Study B

Bdeltaone

event/censoring indicators in the treatment group in Study B

Bsone

surrogate marker values in the treatment group in Study B, NA for individuals not observable at the time the surrogate marker was measured

Bxzero

observed event times in the control group in Study B

Bdeltazero

event/censoring indicators in the control group in Study B

Bszero

surrogate marker values in the control group in Study B, NA for individuals not observable at the time the surrogate marker was measured

t

time of interest

landmark

landmark time of interest, t0

extrapolate

TRUE or FALSE; indicates whether local constant extrapolation should be used, default is TRUE

Value

Variance estimate for \(\sqrt{n_{B}}\hat{\Delta}_{EB}(t,t_0)\)

Details

Variance estimation using the closed form expression under the null hypothesis of no treatment effect; more details are included in the documentation for early.delta.test.

References

Parast L, Cai T, Tian L (2019). Using a Surrogate Marker for Early Testing of a Treatment Effect. Biometrics, 75(4):1253-1263.

Examples

# NOT RUN {
data(dataA)
data(dataB)

var.delta.eb(Axzero = dataA$x0, Adeltazero = dataA$delta0, 
Aszero = dataA$s0, Bxone = dataB$x1, Bdeltaone = dataB$delta1, 
Bsone = dataB$s1, Bxzero = dataB$x0, Bdeltazero = dataB$delta0, 
Bszero = dataB$s0, t=1, landmark=0.5,  extrapolate = TRUE)

# }