nnr_test

Runs the test of non-negative residual treatment effect given two sets of surrogate and primary endpoint values.

internal

Provides functions to nonparametrically assess assumptions necessary to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of regression functions, and non-negative residual treatment effects. More details are available in Hsiao et al 2025 (under review). A tutorial for this package can be found at <https://laylaparast.com/home/SurrogateParadoxTest.html>.

Emily Hsiao

SurrogateParadoxTest

Empirical Testing of Surrogate Paradox Assumptions

nnr_test function

<dl> <dt>s0</dt>
<dd>Vector of surrogate values in control group.</dd> <dt>y0</dt>
<dd>Vector of endpoint values in control group.</dd> <dt>s1</dt>
<dd>Vector of surrogate values in treatment group.</dd> <dt>y1</dt>
<dd>Vector of endpoint values in treatment group.</dd> <dt>n_bootstrap</dt>
<dd>Desired number of bootstrap samples.</dd> <dt>alpha</dt>
<dd>Desired alpha level of test.</dd></dl>

Arguments

Author

Non-negative residual treatment effect function — nnr_test

<dl>

 <dt>s0</dt>
<dd>Vector of surrogate values in control group.</dd>

 <dt>y0</dt>
<dd>Vector of endpoint values in control group.</dd>

 <dt>s1</dt>
<dd>Vector of surrogate values in treatment group.</dd>

 <dt>y1</dt>
<dd>Vector of endpoint values in treatment group.</dd>

 <dt>n_bootstrap</dt>
<dd>Desired number of bootstrap samples.</dd>

 <dt>alpha</dt>
<dd>Desired alpha level of test.</dd>

</dl>

nnr_test: Non-negative residual treatment effect function

Description

Usage

Value

Arguments

Author

References