An example data frame containing individual patient data from the intervention study, with baseline characteristic variables for matching.

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)

IPD function

A data frame with X rows and Y variables:<dl>
 <dt>SUBJID</dt>
<dd>Subject Unique Identifier.</dd> <dt>TRT</dt>
<dd>Grouping variable, e.g., "active" or "control".</dd> <dt>BPDL1</dt>
<dd>Stratification factors for stratified analysis.</dd> <dt>CNSBRAIN</dt>
<dd>Stratification factors for stratified analysis.</dd> <dt>AGEGR</dt>
<dd>Stratification factors for stratified analysis.</dd> <dt>WEIGHT</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd> <dt>HEIGHT</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd> <dt>BMI</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd> <dt>DIAG</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd> <dt>SEX</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd> <dt>ECOG</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd> <dt>SMK</dt>
<dd>Baseline characteristics variables for matching.</dd> <dt>METBRAIN</dt>
<dd>Baseline characteristics variables for matching.</dd> <dt>METLIVER</dt>
<dd>Baseline characteristics variables for matching.</dd> <dt>PARAMCD</dt>
<dd>Subsets to be analyzed, e.g., "PFSINV", "OS".</dd> <dt>AVAL</dt>
<dd>Survival or follow up time.</dd> <dt>CNSR</dt>
<dd>The status indicator, 0 = event, 1 = censored.</dd> <dt>RESP</dt>
<dd>Response status, 1 = responder, 0 = non-responder.</dd> 
</dl>

Format

Description of IPD dataset — IPD

A data frame with X rows and Y variables:<dl>
 <dt>SUBJID</dt>
<dd>Subject Unique Identifier.</dd>

 <dt>TRT</dt>
<dd>Grouping variable, e.g., "active" or "control".</dd>

 <dt>BPDL1</dt>
<dd>Stratification factors for stratified analysis.</dd>

 <dt>CNSBRAIN</dt>
<dd>Stratification factors for stratified analysis.</dd>

 <dt>AGEGR</dt>
<dd>Stratification factors for stratified analysis.</dd>

 <dt>WEIGHT</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd>

 <dt>HEIGHT</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd>

 <dt>BMI</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd>

 <dt>DIAG</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd>

 <dt>SEX</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd>

 <dt>ECOG</dt>
<dd>Baseline characteristic variables for matching or summarizing.</dd>

 <dt>SMK</dt>
<dd>Baseline characteristics variables for matching.</dd>

 <dt>METBRAIN</dt>
<dd>Baseline characteristics variables for matching.</dd>

 <dt>METLIVER</dt>
<dd>Baseline characteristics variables for matching.</dd>

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

 <dt>AVAL</dt>
<dd>Survival or follow up time.</dd>

 <dt>CNSR</dt>
<dd>The status indicator, 0 = event, 1 = censored.</dd>

 <dt>RESP</dt>
<dd>Response status, 1 = responder, 0 = non-responder.</dd>

 
</dl>

IPD: Description of IPD dataset

Description

Usage

Arguments

Format

Examples