# delta.s.surv.estimate: 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 surrogate marker information measured at t_0 and primary outcome information up to t_0 i.e. the hypothetical treatment effect if both the surrogate marker distribution at t_0 and survival up to t_0 in the treatment group look like the surrogate marker distribution 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

 1 2 delta.s.surv.estimate(xone, xzero, deltaone, deltazero, sone, szero, t, weight.perturb = NULL, landmark, extrapolate = FALSE, transform = FALSE) 

## 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

## Details

Details are included in the documentation for R.s.surv.estimate.

## Value

\hat{Δ}_S(t,t_0), the robust residual treatment effect estimate accounting for surrogate marker information 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". If the treatment effect is negative, the user will receive the following message: "Warning: it looks like you need to switch the treatment groups" as this package assumes throughout that larger values of the event time are better. If the observed support of the surrogate marker for the control group is outside the observed support of the surrogate marker for the treatment group, the user will receive the following message: "Warning: observed supports do not appear equal, may need to consider a transformation or extrapolation".

Layla Parast

## References

Parast L, Cai T and Tian L. Evaluating Surrogate Marker Information using Censored Data. Under Review.

## Examples

 1 2 data(d_example_surv) names(d_example_surv) 

Rsurrogate documentation built on May 29, 2017, 6:16 p.m.