resam

Resamples data to obtain standard error estimates

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

resam function

<dl> <dt>vv</dt>
<dd>random exponential weight</dd> <dt>t</dt>
<dd>number, the time of interest</dd> <dt>t.0</dt>
<dd>landmark time of interest, \(t_0\)</dd> <dt>tt</dt>
<dd>set of time points between t.0 and t</dd> <dt>data</dt>
<dd>full dataset</dd> <dt>data1</dt>
<dd>a random half from data</dd> <dt>data2</dt>
<dd>the remaining random half from data</dd> <dt>indexindex</dt>
<dd>the indices of data contained within data1</dd></dl>

Arguments

Author

Resampling for standard error estimation — resam

<dl>

 <dt>vv</dt>
<dd>random exponential weight</dd>

 <dt>t</dt>
<dd>number, the time of interest</dd>

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

 <dt>tt</dt>
<dd>set of time points between t.0 and t</dd>

 <dt>data</dt>
<dd>full dataset</dd>

 <dt>data1</dt>
<dd>a random half from data</dd>

 <dt>data2</dt>
<dd>the remaining random half from data</dd>

 <dt>indexindex</dt>
<dd>the indices of data contained within data1</dd>

</dl>

resam: Resampling for standard error estimation

Description

Usage

Value

Arguments

Author