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

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

Calculates the conditional probability of survival for control group values

Description

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

Usage

pred.smooth.surv(xone.f, deltaone.f, sone.f, szero.one, myt, weight.pred, 
extrapolate, transform, ps.weight = NULL)

Arguments

xone.f	observed event times in the treatment group
deltaone.f	censoring indicators in the treatment group
sone.f	surrogate marker values in the treatment group
szero.one	surrogate marker values in the control group
myt	time of interest
weight.pred	weight used for perturbation resampling
extrapolate	TRUE or FALSE; indicates whether local constant extrapolation should be used, default is FALSE
transform	TRUE or FALSE; indicates whether a transformation should be used, default is FALSE
ps.weight	vector of propensity score weights to be used in the multiple marker setting, default is NULL

Details

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

Value

conditional probability of survival past t for control group

Author(s)

Layla Parast

laylaparast/Rsurrogate documentation built on Sept. 28, 2022, 12:25 p.m.