delta.multiple.surv: Calculates robust residual treatment effect accounting for...

In Rsurrogate: Robust Estimation of the Proportion of Treatment Effect Explained by Surrogate Marker Information

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)

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.

Details

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

Value

\hat{Δ}_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.

Note

If the treatment effect is not significant, the user will receive the following message: "Warning: it looks like the treatment effect is not significant; may be difficult to interpret the residual treatment effect in this setting".

Author(s)

Layla Parast

References

Details can be found in Parast, L., Cai, T., Tian, L. (2021). Evaluating Multiple Surrogate Markers with Censored Data. Biometrics, In press.

Examples

data(d_example_multiple)
names(d_example_multiple)
## Not run: 
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)

## End(Not run)

Rsurrogate documentation built on Nov. 14, 2021, 9:07 a.m.