polyMAIC

Overview

Matching-Adjusted Indirect Comparison (MAIC) is a technique used to account for the differences in baseline characteristics between treatment groups in indirect comparisons, especially when individual patient data are available for one treatment, but only aggregate-level data exist for the comparator. The PolyMAIC method, proposed by Alsop and Pont¹, uses fourth order polynomial functions to estimate individual patient weights (IPW).

Features

This package:

1. Contains the simulated data used in Alsop and Pont¹.
2. Estimates IPWs.
3. Provides matching algorithm diagnostics.
4. Produces a histogram of re-scaled IPWs.

Usage

Installation

Install the development version using: devtools::install_github("Numerus Ltd/polyMAIC")

This package has not been uploaded to the CRAN repository.

Dependencies

The polymaic() package is dependant upon the following packages:

• nloptr()
• Hmisc()
• tictoc()
• haven()
• stringr()
• ggplot2()

Bibliography

1. Alsop JC, Pont LO. Matching-adjusted indirect comparison via a polynomial-based non-linear optimization method. J Comp Eff Res. 2022 Jun;11(8):551-561. doi: 10.2217/cer-2021-0266. Epub 2022 May 4. PMID: 35506464.
2. Signorovitch J, Wu E, Yu A et al. Comparative effectiveness without head-to-head trials: a method for matching-adjusted indirect comparisons applied to psoriasis treatment with adalimumab oretanercept. Pharmacoeconomics 28(10), 935–945 (2010).
3. Johnson S.G. SLSQP algorithm in NLopt. https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#slsqp.
4. Johnson S (2008). The NLopt nonlinear-optimization package. https://github.com/stevengj/nlopt.

Citation

Developers: J. Wilson, C. Reynish, L. Pont, J. Alsop.
Initial Upload Date: 06-06-2025
For any enquires please contact [email protected]