AgD_eff

An example data frame containing aggregate results data from the comparator study.

A generalised workflow for Matching-Adjusted Indirect
Comparison (MAIC) analysis, which supports both anchored and
non-anchored MAIC methods. In MAIC, unbiased trial outcome comparison
is achieved by weighting the subject-level outcomes of the
intervention trial so that the weighted aggregate measures of
prognostic or effect-modifying variables match those of the comparator
trial. Measurements supported include time-to-event (e.g., overall
survival) and binary (e.g., objective tumor response). The method is
described in Signorovitch et al. (2010)
<doi:10.2165/11538370-000000000-00000> and Signorovitch et al. (2012)
<doi:10.1016/j.jval.2012.05.004>.

Xiao Qi

MAICtools

Performing Matched-Adjusted Indirect Comparisons (MAIC)

AgD_eff function

A data frame with X rows and Y variables:<dl>
 <dt>STUDY</dt>
<dd>Label of the comparator study, e.g., "Study XX-1".</dd> <dt>PARAM</dt>
<dd>Subsets to be analyzed, e.g., "PFSINV", "OS".</dd> <dt>EST</dt>
<dd>Point estimate of the effect size.</dd> <dt>CIL</dt>
<dd>The lower confidence limit of the point estimate of the effect size.</dd> <dt>CIU</dt>
<dd>The upper confidence limit of the point estimate of the effect size.</dd> 
</dl>

Format

Description of AgD_eff dataset — AgD_eff

A data frame with X rows and Y variables:<dl>
 <dt>STUDY</dt>
<dd>Label of the comparator study, e.g., "Study XX-1".</dd>

 <dt>PARAM</dt>
<dd>Subsets to be analyzed, e.g., "PFSINV", "OS".</dd>

 <dt>EST</dt>
<dd>Point estimate of the effect size.</dd>

 <dt>CIL</dt>
<dd>The lower confidence limit of the point estimate of the effect size.</dd>

 <dt>CIU</dt>
<dd>The upper confidence limit of the point estimate of the effect size.</dd>

 
</dl>

AgD_eff: Description of AgD_eff dataset

Description

Usage

Arguments

Format

Examples