cohetsurr (version 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.

Description

Assesses surrogate heterogeneity in real world data by estimating the proportion of the treatment effect explained as a function of baseline covariates. Optionally tests individuals for strong surrogacy based on a threshold.

Usage

obs.het.surr(df.train, df.test, type, var.want = FALSE, threshold = NULL, 
  use.actual.control.S = FALSE)

Value

A dataframe is returned, which is the df.test argument with new columns appended for the estimates and corresponding variances of delta, delta.s, and R.s. If a threshold is specified, returns a p-value for the null hypothesis that PTE > threshold.

Arguments

df.train

dataframe containing training data; must have columns G (treatment assignment), S (surrogate marker), and Y (primary outcome), in addition to the baseline covariates of interest

df.test

dataframe containing testing data; must contain the same baseline covariate columns as the training data

type

options are "linear", "gam", "trees", or "all"; type of base learners to use

var.want

TRUE or FALSE, if variance estimates are wanted

threshold

optional threshold to test individuals for the null hypothesis that PTE is greater than the threshold; must have var.want = TRUE to return p-values

use.actual.control.S

TRUE or FALSE, if user prefers to use the actual observed values for the surrogate in the control group instead of predicting values from the base learners

Author

Rebecca Knowlton

References

Knowlton, R. and Parast, L. (2025) ``Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners." Under Review.

Examples

data(obs_exampledata_train)
data(obs_exampledata_test)
obs.het.surr(df.train = obs_exampledata_train, df.test = obs_exampledata_test,
type = "linear", var.want = FALSE)