Geometric approaches to assessing the numerical feasibility for conducting matching-adjusted indirect comparisons

TL;DR

This paper introduces geometric and multivariate data analysis methods to evaluate the feasibility of matching-adjusted indirect comparisons (MAIC), aiming to prevent misleading results and understand discrepancies in MAIC analyses.

Contribution

The paper presents novel geometric approaches and multivariate techniques to assess the appropriateness of MAIC for specific datasets and investigates intrinsic properties affecting MAIC weight estimation.

Findings

Methods identify when no numerical solutions are possible with MAIC.

Highlight potential causes for conflicting MAIC results between teams.

Show that properties of MAIC weights can influence analysis outcomes.

Abstract

We discuss how to handle matching-adjusted indirect comparison (MAIC) from a data analyst's perspective. We introduce several multivariate data analysis methods to assess the appropriateness of MAIC for a given data set. These methods focus on comparing the baseline variables used in the matching from a study that provides the summary statistics, or aggregated data (AD) and a study that provides individual patient level data (IPD). The methods identify situations when no numerical solutions are possible with the MAIC method. This helps to avoid misleading results being produced. Moreover, it has been observed that sometimes contradicting results are reported by two sets of MAIC analyses produced by two teams, each having their own IPD and applying MAIC using the AD published by the other team. We show that an intrinsic property of the MAIC estimated weights can be a contributing factor for this phenomenon.