cohetsurr (version 2.0)

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.

Description

Assesses complex heterogeneity in the utility of a surrogate marker by estimating the proportion of treatment effect explained by the surrogate marker as a function of multiple baseline covariates in a randomized treatment setting. Optionally, tests for evidence of heterogeneity overall and flags regions where the proportion of treatment effect explained is above a given threshold.

Usage

complex.heterogeneity(y, s, a, W.mat, type = "model", variance = FALSE, 
test = FALSE, W.grid = NULL, grid.size = 4, threshold = NULL)

Value

A list is returned:

return.grid

grid of estimates for the overall treatment effect, the residual treatment effect, and the proportion of treatment effect explained as a function of the baseline covariates, W. Includes variance estimates and regions flagged above the threshold, if specified by the user.

pval

p-value(s) from the F test and the two step omnibus test for heterogeneity, depending on type argument.

Arguments

y

y, the outcome

s

s, the surrogate marker

a

a, the treatment assignment with 1 indicating the treatment group and 0 indicating the control group, assumed to be randomized

W.mat

matrix of baseline covariate observations, where the first column is W1, second columns is W2, etc.

type

options are "model", "two step", or "both"; specifies the estimation method that should be used for the proportion of treatment effect explained

variance

TRUE or FALSE, if variance/standard error estimates are wanted

test

TRUE or FALSE, if test for heterogeneity is wanted

W.grid

grid for the baseline covariates W where estimation will be provided

grid.size

number of measures for each baseline covariate to include in the estimation grid, if one is not provided by the user directly

threshold

threshold to flag regions where the estimated proportion of the treatment effect explained is at least that high

Author

Rebecca Knowlton

References

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.

Examples

  data(exampledata)
  names(exampledata)
  complex.heterogeneity(y = exampledata$y,
                        s = exampledata$s,
                        a = exampledata$a,
                        W.mat = matrix(cbind(exampledata$w1, exampledata$w2), ncol = 2),
                        type = "model",
                        W.grid = matrix(cbind(exampledata$w1.grid, exampledata$w2.grid),ncol=2))