Three artificial scenarios serves as the ad cases.
This is used in Glimm &amp; Yau (2021)

datasets

The second version (0.2.0) contains implementation for exact matching which is an alternative to propensity score matching (see Glimm & Yau (2025)). The initial version (0.1.2) contains a collection of easy-to-implement tools for checking whether a MAIC can be conducted, as well as an alternative way of calculating weights (see Glimm & Yau (2021) <doi:10.1002/pst.2210>.)

Lillian Yau

maicChecks

Exact Matching and Matching-Adjusted Indirect Comparison (MAIC)

Ekkehard Glimm

Xinlei Deng

eAD function

<dl>
<dt>scen</dt>
<dd>corresponds to scenarios A, B, and C in the reference manuscript (Glimm &amp; Yau (2021)). Scenario A is very close to IPD center (see data(ipd)) and is within the IPD convex hull; scenario B is further away from IPD center but otherwise still inside the IPD convex hull; scenario C is outside IPD convex hull.</dd><dt>y1</dt>
<dd>a numeric vector</dd><dt>y2</dt>
<dd>a numeric vector</dd>
</dl>

Format

three AD scenarios — eAD

<dl>
<dt>scen</dt>
<dd>corresponds to scenarios A, B, and C in the reference manuscript (Glimm &amp; Yau (2021)). Scenario A is very close to IPD center (see data(ipd)) and is within the IPD convex hull; scenario B is further away from IPD center but otherwise still inside the IPD convex hull; scenario C is outside IPD convex hull.</dd>

<dt>y1</dt>
<dd>a numeric vector</dd>

<dt>y2</dt>
<dd>a numeric vector</dd>


</dl>

eAD: three AD scenarios

Description

Usage

Arguments

Format

References

Examples