Package 'CMFsurrogate'

Title: Calibrated Model Fusion Approach to Combine Surrogate Markers
Description: Uses a calibrated model fusion approach to optimally combine multiple surrogate markers. Specifically, two initial estimates of optimal composite scores of the markers are obtained; the optimal calibrated combination of the two estimated scores is then constructed which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained (PTE) by the final combined score. The primary function, pte.estimate.multiple(), estimates the PTE of the identified combination of multiple surrogate markers. Details are described in Wang et al (2022) <doi:10.1111/biom.13677>.
Authors: Xuan Wang [aut], Layla Parast [cre]
Maintainer: Layla Parast <[email protected]>
License: GPL
Version: 1.0
Built: 2025-02-14 02:39:13 UTC
Source: https://github.com/cran/CMFsurrogate

Help Index

Example data

Description

Example data

Usage

data("example.data")

Format

A list with 3 elements:

sob

the surrogate markers

yob

the primary outcome

aob

the treatment indicator

Examples

data(example.data)
names(example.data)

Generate bootstrap sample

Description

Generate bootstrap sample

Usage

gen.bootstrap.weights(n, num.perturb = 500)

Arguments

n

sample size

num.perturb

number of replicates/resamples

Value

matrix with n rows and num.perturb columns of indeces

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

Estimates quantities using resampled data

Description

Estimates quantities using resampled data

Usage

resam(index, yob, sob, aob, n)

Arguments

index

index

yob

y

sob

surrogates

aob

treatment

n

n

Value

Outputs parametric estimate, additive linear estimate, and convex combination estimate