pte.estimate.multiple {CMFsurrogate}

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)

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

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

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