CMFsurrogate (version 1.1)

pte.estimate.multiple: Estimates the proportion of treatment effect explained by the optimal combination of multiple surrogate markers using a calibrated model fusion approach

Description

Estimates the proportion of treatment effect explained by the optimal combination of multiple surrogate markers using a calibrated model fusion approach

Usage

pte.estimate.multiple(sob, yob, aob, var = TRUE, rep = 500)

Value

pte.es

Estimate of the proportion of treatment effect explained (PTE)

pte.se

if var = TRUE, estimate of the standard error of the PTE

Arguments

sob

surrogates

yob

primary outcome, y

aob

treatment indicator

var

TRUE or FALSE, if variance/SE of PTE is being requested

rep

if var is TRUE, number of resampled draws to use for bootstrap

References

Wang, X., Parast, L., Han, L., Tian, L., & Cai, T. (2022). Robust approach to combining multiple markers to improve surrogacy. Biometrics, In press.

Examples

data(example.data)
out=pte.estimate.multiple(sob=example.data$sob, yob=example.data$yob, 
aob=example.data$aob, var = FALSE)
out