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MAICtools (version 0.1.1)

Performing Matched-Adjusted Indirect Comparisons (MAIC)

Description

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) and Signorovitch et al. (2012) .

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Install

install.packages('MAICtools')

Monthly Downloads

207

Version

0.1.1

License

MIT + file LICENSE

Maintainer

Xiao Qi

Last Published

December 11th, 2024

Functions in MAICtools (0.1.1)

AgD_bl

Description of AgD_bl dataset
estimate_ess

Estimate Effective Sample Size (ESS)
check_matching

Check Whether the Variables are Balanced After Weighting
IPD

Description of IPD dataset
estimate_weights

Functions for the Estimation of Propensity Weights
check_matching2wider

Convert a Longer Table Generated by check_matching() Into a Wider Table
hist_weights

Histograms of Weights and Rescaled Weights Distributions
pseudo

Description of pseudo dataset
AgD_eff

Description of AgD_eff dataset
anchored_maic

Conduct Anchored Matching-Adjusted Indirect Comparison (MAIC).
pts

Description of pts dataset
summarize_weights

Summarize the Distribution of Weight Values
unanchored_kmplot

Generate a Kaplan-Meier Plot with Individual Efficacy Data and Pseudo Efficacy Data.
unanchored_maic

Conduct non-Anchored Matching-Adjusted Indirect Comparison (MAIC).
unanchored_maic_bootstrap

Conduct non-Anchored Matching-Adjusted Indirect Comparison (MAIC) and Calculate Confidence Intervals (CIs) Using Bootstrap.
unpts

Description of unpts dataset