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Matching-Adjusted Indirect Comparison (MAIC): An Established Methodology for Comparing Treatment Efficacies Across Trials

In the realm of comparative effectiveness research (CER), decision-makers often face challenges when attempting to compare treatment efficacies due to limited availability of comparative data, especially for new therapies. This white paper introduces the Matching-Adjusted Indirect Comparison (MAIC) methodology as a powerful technique for addressing these evidence gaps.

MAIC combines individual patient data (IPD) from one treatment with published aggregate data from another, allowing for greater adjustment of observed cross-trial differences. By reweighting patients and performing matching based on odds, MAIC enables the comparison of treatment outcomes between balanced populations. This approach has been successfully applied in various therapeutic areas, providing valuable insights before the publication of randomized comparative studies. While MAIC offers significant advantages in addressing evidence gaps, it also comes with certain limitations that should be carefully considered when conducting indirect comparison analyses. Nonetheless, for new and emerging therapies lacking extensive comparable studies, MAIC offers robust support for informed decision-making by payers and health technology assessment authorities.

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Mahendra Rai
Senior Director, HEOR

Mahendra has over 15 years of experience in outcomes research, health economics, real world insights and observational research spanning the life science spectrum of pharmaceuticals, medical devices, diagnostics, and OTC. Prior to joining EVERSANA,…

Ivan Tjong-A-Hung
Director HEOR & RWE for China

Dr. Ivan Tjong-A-Hung is a business leader for Health Economic and Outcomes Research & Real-World Evidence (HEOR and RWE) with over 30 years of experience in the Healthcare Industry. At EVERSANA China, he leads…