Rsurrogate (version 3.2)

delta.multiple.surv: Calculates robust residual treatment effect accounting for multiple surrogate markers at a specified time and primary outcome information up to that specified time

Description

This function calculates the robust estimate of the residual treatment effect accounting for multiple surrogate markers measured at \(t_0\) and primary outcome information up to \(t_0\) i.e. the hypothetical treatment effect if both the surrogate marker distributions at \(t_0\) and survival up to \(t_0\) in the treatment group look like the surrogate marker distributions and survival up to \(t_0\) in the control group. Ideally this function is only used as a helper function and is not directly called.

Usage

delta.multiple.surv(xone, xzero, deltaone, deltazero, sone, szero, type =1, t, 
weight.perturb = NULL, landmark, extrapolate = FALSE, transform = FALSE,
approx = T)

Value

\(\hat{\Delta}_S(t,t_0)\), the residual treatment effect estimate accounting for multiple surrogarte markers measured at \(t_0\) and primary outcome information up to \(t_0\).

Arguments

xone

numeric vector, the observed event times in the treatment group, X = min(T,C) where T is the time of the primary outcome and C is the censoring time.

xzero

numeric vector, the observed event times in the control group, X = min(T,C) where T is the time of the primary outcome and C is the censoring time.

deltaone

numeric vector, the event indicators for the treatment group, D = I(T<C) where T is the time of the primary outcome and C is the censoring time.

deltazero

numeric vector, the event indicators for the control group, D = I(T<C) where T is the time of the primary outcome and C is the censoring time.

sone

matrix of numeric values; surrogate marker measurements at \(t_0\) for treated observations. If \(X_{1i}<t_0\), then the surrogate marker measurements should be NA.

szero

matrix of numeric values; surrogate marker measurements at \(t_0\) for control observations. If \(X_{0i}<t_0\), then the surrogate marker measurements should be NA.

type

type of estimate; options are 1 = two-stage robust estimator, 2 = weighted two-stage robust estimator, 3 = double-robust estimator, 4 = two-stage model-based estimator, 5 = weighted estimator, 6 = double-robust model-bsed estimator; default is 1.

t

the time of interest.

weight.perturb

weights used for perturbation resampling.

landmark

the landmark time \(t_0\) or time of surrogate marker measurement.

extrapolate

TRUE or FALSE; indicates whether the user wants to use extrapolation.

transform

TRUE or FALSE; indicates whether the user wants to use a transformation for the surrogate marker psuedo-score.

approx

TRUE or FALSE indicating whether an approximation should be used when calculating the probability of censoring; most relevant in settings where the survival time of interest for the primary outcome is greater than the last observed event but before the last censored case, default is TRUE.

Author

Layla Parast

Details

Details are included in the documentation for R.multiple.surv.

References

Parast, L., Cai, T., & Tian, L. (2021). Evaluating multiple surrogate markers with censored data. Biometrics, 77(4), 1315-1327.

Examples

data(d_example_multiple)
names(d_example_multiple)
if (FALSE) {
delta.multiple.surv(xone = d_example_multiple$x1, xzero = d_example_multiple$x0, deltaone =
 d_example_multiple$delta1, deltazero = d_example_multiple$delta0, sone = 
 as.matrix(d_example_multiple$s1), szero = as.matrix(d_example_multiple$s0), 
 type =1, t = 1, landmark=0.5)
}