maic.boot

Matching-adjusted indirect comparison bootstrap sampling.

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

maic.boot function

<dl><dt>ipd</dt>
<dd>Individual-level patient data. Dataframe with one row per patient with outcome, treatment and covariate columns.</dd>
<dt>indices</dt>
<dd>Vector of indices, same length as original,
which define the bootstrap sample</dd>
<dt>formula</dt>
<dd>Linear regression <code>formula</code> object. Prognostic factors (PF) are main effects and effect modifiers (EM) are
interactions with the treatment variable, e.g., y ~ X1 + trt + trt:X2. For covariates as both PF and EM use <code>*</code> syntax.</dd>
<dt>family</dt>
<dd>A 'family' object specifying the distribution and link function (e.g., 'binomial').
See stats::family() for more details.</dd>
<dt>ald</dt>
<dd>Aggregate-level data. Long format summary statistics for each covariate and treatment outcomes.
We assume a common distribution for each treatment arm.</dd>
<dt>hat_w</dt>
<dd>MAIC weights; default <code>NULL</code> which calls <code>maic_weights()</code></dd></dl>

Arguments

MAIC bootstrap sample — maic.boot

<dl>

<dt>ipd</dt>
<dd>Individual-level patient data. Dataframe with one row per patient with outcome, treatment and covariate columns.</dd>


<dt>indices</dt>
<dd>Vector of indices, same length as original,
which define the bootstrap sample</dd>


<dt>formula</dt>
<dd>Linear regression <code>formula</code> object. Prognostic factors (PF) are main effects and effect modifiers (EM) are
interactions with the treatment variable, e.g., y ~ X1 + trt + trt:X2. For covariates as both PF and EM use <code>*</code> syntax.</dd>


<dt>family</dt>
<dd>A 'family' object specifying the distribution and link function (e.g., 'binomial').
See stats::family() for more details.</dd>


<dt>ald</dt>
<dd>Aggregate-level data. Long format summary statistics for each covariate and treatment outcomes.
We assume a common distribution for each treatment arm.</dd>


<dt>hat_w</dt>
<dd>MAIC weights; default <code>NULL</code> which calls <code>maic_weights()</code></dd>

</dl>

maic.boot: MAIC bootstrap sample

Description

Usage

Value

Arguments

See Also