var.delta.eb {SurrogateTest} | R Documentation |
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 |
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.
Value
Variance estimate for \sqrt{n_{B}}\hat{\Delta}_{EB}(t,t_0)
Author(s)
Layla Parast
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
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)