WEIGHT

Creates a matrix of weights and estimates to be used in estimation of the PTE within R.opt.event

internal

Estimates the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1> and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" <doi:10.1002/sim.70268>. A tutorial for this package can be found at <https://www.laylaparast.com/surrogateoutcome>.

Layla Parast

SurrogateOutcome

Estimation of the Proportion of Treatment Effect Explained by
Surrogate Outcome Information

Xuan Wang

WEIGHT function

<dl> <dt>xob</dt>
<dd>numeric vector, the observed event time: X = min(T, C) where T is the time of the primary outcome, C is the censoring time</dd> <dt>deltaob</dt>
<dd>numeric vector of 0/1, the censoring indicator: D = I(T&lt;C) where T is the time of the primary outcome, C is the censoring time</dd> <dt>aob</dt>
<dd>numeric vector of 0/1, indicating treatment group</dd> <dt>n</dt>
<dd>sample size</dd> <dt>t.0</dt>
<dd>landmark time of interest, \(t_0\)</dd> <dt>t</dt>
<dd>time of interest for treatment effect.</dd></dl>

Arguments

Author

Weight function — WEIGHT

<dl>

 <dt>xob</dt>
<dd>numeric vector, the observed event time: X = min(T, C) where T is the time of the primary outcome, C is the censoring time</dd>

 <dt>deltaob</dt>
<dd>numeric vector of 0/1, the censoring indicator: D = I(T&lt;C) where T is the time of the primary outcome, C is the censoring time</dd>

 <dt>aob</dt>
<dd>numeric vector of 0/1, indicating treatment group</dd>

 <dt>n</dt>
<dd>sample size</dd>

 <dt>t.0</dt>
<dd>landmark time of interest, \(t_0\)</dd>

 <dt>t</dt>
<dd>time of interest for treatment effect.</dd>

</dl>

WEIGHT: Weight function

Description

Usage

Value

Arguments

Author