Assessing Complex Heterogeneity in Surrogacy
Performs bootstrap estimation procedures for the variance of the propo...
Estimates the proportion of treatment effect explained by the surrogat...
Calculate bootstrapped variance estimates in an observational setting.
Estimate the proportion of the treatment effect explained in an observ...
Estimate the proportion of the treatment effect explained by the surro...
Estimates the proportion of treatment effect explained as a function o...
Estimates the proportion of treatment effect explained as a function o...
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 <doi:10.1002/sim.70001>. 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 <https://www.laylaparast.com/cohetsurr>.