pte.estimate.multiple {CMFsurrogate} R Documentation

## 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


[Package CMFsurrogate version 1.0 Index]