pred.smooth.surv

observed event times in the treatment group.

xone.f

censoring indicators in the treatment group.

deltaone.f

surrogate marker values in the treatment group.

sone.f

surrogate marker values in the control group.

szero.one

time of interest.

weight used for perturbation resampling.

weight

TRUE or FALSE; indicates whether a transformation should be used, default is FALSE.

transform

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

extrapolate

Helper function; calculates the estimated probability of survival for control 
control group surrogate values using treatment group surrogate and 
outcome information.

robust

internal

smooth

nonparametric

survival

Provides functions to estimate the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, Tian, Cai (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1>. The main functions are (1) R.q.event() which calculates the proportion of the treatment effect (the difference in restricted mean survival time at time t) explained by surrogate outcome information observed up to a selected landmark time, (2) R.t.estimate() which calculates the proportion of the treatment effect explained by primary outcome information only observed up to a selected landmark time, and (3) IV.event() which calculates the incremental value of the surrogate outcome information.

pred.smooth.surv: Calculates the conditional probability of survival for control group values

Description

Usage

Arguments

Value