get.mu.hat.0

Calculates the conditional expectation of the primary outcome from the control group in Study A, given the surrogate outcome, using kernel smoothing.

internal

Provides functions for treatment effect estimation, hypothesis testing, and future study design for settings where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully measured in the remaining patients. More details are available in: Knowlton, R., Parast, L. (2024) ``Efficient Testing Using Surrogate Information," Biometrical Journal, 67(6): e70086, <doi:10.1002/bimj.70086>. A tutorial for this package can be found at <https://www.laylaparast.com/etsi>.

Layla Parast

etsi

Efficient Testing Using Surrogate Information

Rebecca Knowlton

get.mu.hat.0 function

<dl> <dt>s</dt>
<dd>s, the surrogate outcome</dd> <dt>A.s0</dt>
<dd>A.s0, the vector of surrogate outcomes from the control group in Study A</dd> <dt>A.y0</dt>
<dd>A.y0, the vector of primary outcomes from the control group in Study A</dd></dl>

Arguments

Calculates the conditional expectation of the primary outcome from the control group in Study A, given the surrogate outcome, using kernel smoothing. — get.mu.hat.0

<dl>

 <dt>s</dt>
<dd>s, the surrogate outcome</dd>

 <dt>A.s0</dt>
<dd>A.s0, the vector of surrogate outcomes from the control group in Study A</dd>

 <dt>A.y0</dt>
<dd>A.y0, the vector of primary outcomes from the control group in Study A</dd>

</dl>

get.mu.hat.0: Calculates the conditional expectation of the primary outcome from the control group in Study A, given the surrogate outcome, using kernel smoothing.

Description

Usage

Value

Arguments