two.step.est

Estimates the proportion of treatment effect explained as a function of multiple baseline covariates, W, using a two step, semiparametric model in a randomized treatment setting.

internal

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>.

Layla Parast

cohetsurr

Assessing Complex Heterogeneity in Surrogacy

Rebecca Knowlton

two.step.est function

<dl> <dt>data.control</dt>
<dd>dataframe containing data from the control group, specifically the outcome Y, the surrogate S, and the baseline covariates W</dd> <dt>data.treat</dt>
<dd>dataframe containing data from the treamtent group, specifically the outcome Y, the surrogate S, and the baseline covariates W</dd> <dt>W.grid.expand.function</dt>
<dd>expanded version of the W grid of baseline covariates, where each row is a specific combination of the covariates for which the estimates should be provided</dd></dl>

Arguments

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

<dl>

 <dt>data.control</dt>
<dd>dataframe containing data from the control group, specifically the outcome Y, the surrogate S, and the baseline covariates W</dd>

 <dt>data.treat</dt>
<dd>dataframe containing data from the treamtent group, specifically the outcome Y, the surrogate S, and the baseline covariates W</dd>

 <dt>W.grid.expand.function</dt>
<dd>expanded version of the W grid of baseline covariates, where each row is a specific combination of the covariates for which the estimates should be provided</dd>

</dl>

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

Description

Usage

Value

Arguments