maic: Matching-Adjusted Indirect Comparison

A generalised workflow for generation of subject weights to be used in Matching-Adjusted Indirect Comparison (MAIC) per Signorovitch et al. (2012) <doi:10.1016/j.jval.2012.05.004>, Signorovitch et al (2010) <doi:10.2165/11538370-000000000-00000>. In MAIC, unbiased comparison between outcomes of two trials is facilitated by weighting the subject-level outcomes of one trial with weights derived such that the weighted aggregate measures of the prognostic or effect modifying variables are equal to those of the sample in the comparator trial. The functions and classes included in this package wrap and abstract the process demonstrated in the UK National Institute for Health and Care Excellence Decision Support Unit (NICE DSU)'s example (Phillippo et al, (2016) [see URL]), providing a repeatable and easily specifiable workflow for producing multiple comparison variable sets against a variety of target studies, with preprocessing for a number of aggregate target forms (e.g. mean, median, domain limits).

Version: 0.1.4
Depends: R (≥ 3.0.0)
Imports: Hmisc, matrixStats, weights
Suggests: testthat
Published: 2022-04-27
DOI: 10.32614/CRAN.package.maic
Author: Rob Young [aut, cre]
Maintainer: Rob Young <rob.young at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
In views: CausalInference
CRAN checks: maic results


Reference manual: maic.pdf


Package source: maic_0.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): maic_0.1.4.tgz, r-oldrel (arm64): maic_0.1.4.tgz, r-release (x86_64): maic_0.1.4.tgz, r-oldrel (x86_64): maic_0.1.4.tgz
Old sources: maic archive


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