var_by_pooling

internal

For the problem of indirect treatment comparison with limited subject-level data, this
package provides tools for model-based standardisation with several different computation approaches.
See Remiro‐Azócar A, Heath A, Baio G (2022) "Parametric G‐computation for compatible
indirect treatment comparisons with limited individual patient data",
Res. Synth. Methods, 1–31. ISSN 1759-2879, <doi:10.1002/jrsm.1565>.

Nathan Green

outstandR

Model-Based Standardisation for Indirect Treatment Comparison
with Limited Subject-Level Data

Chengyang Gao

Antonio Remiro-Azocar

var_by_pooling function

<dl><dt>n_imp</dt>
<dd>Number of syntheses used in analysis stage (high for low Monte Carlo error)</dd>
<dt>bar.v</dt>
<dd>"within" variance (average of variance point estimates)</dd>
<dt>b</dt>
<dd>"between" variance (sample variance of point estimates)</dd></dl>

Arguments

Variance estimate by pooling — var_by_pooling

<dl>

<dt>n_imp</dt>
<dd>Number of syntheses used in analysis stage (high for low Monte Carlo error)</dd>


<dt>bar.v</dt>
<dd>"within" variance (average of variance point estimates)</dd>


<dt>b</dt>
<dd>"between" variance (sample variance of point estimates)</dd>

</dl>

var_by_pooling: Variance estimate by pooling

Description

Usage

Value

Arguments