Learn R Programming

cohetsurr (version 2.0)

Assessing Complex Heterogeneity in Surrogacy

Description

Provides functions to assess complex heterogeneity in the strength of a surrogate marker with respect to multiple baseline covariates, in either a randomized treatment setting or observational setting. For a randomized treatment setting, the functions assess and test for heterogeneity using both a parametric model and a semiparametric two-step model. More details for the randomized setting are available in: Knowlton, R., Tian, L., & Parast, L. (2025). "A General Framework to Assess Complex Heterogeneity in the Strength of a Surrogate Marker," Statistics in Medicine, 44(5), e70001 . For an observational setting, functions in this package assess complex heterogeneity in the strength of a surrogate marker using meta-learners, with options for different base learners. More details for the observational setting will be available in the future in: Knowlton, R., Parast, L. (2025) "Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners." A tutorial for this package can be found at .

Copy Link

Version

Install

install.packages('cohetsurr')

Monthly Downloads

164

Version

2.0

License

GPL

Maintainer

Layla Parast

Last Published

April 11th, 2025

Functions in cohetsurr (2.0)

obs.het.surr

Estimate the proportion of the treatment effect explained by the surrogate marker as a function of multiple baseline covariates in an observational setting.
boot.var

Performs bootstrap estimation procedures for the variance of the proportion of treatment effect explained, the omnibus test, and identifying a region above a treshold.
complex.heterogeneity

Estimates the proportion of treatment effect explained by the surrogate marker as a function of multiple baseline covariates in a randomized treatment setting.
obs_exampledata_test

Example testing data for observational setting
exampledata

Example data
obs_exampledata_train

Example training data for observational setting
two.step.est

Estimates the proportion of treatment effect explained as a function of multiple baseline covariates, W, using a two step, semiparametric model.
obs.estimate.PTE

Estimate the proportion of the treatment effect explained in an observational setting.
parametric.est

Estimates the proportion of treatment effect explained as a function of multiple baseline covariates, W, using a parametric model.
obs.boot.var

Calculate bootstrapped variance estimates in an observational setting.