On matching-adjusted indirect comparison and calibration estimation

TL;DR

This paper explores the connection between matching-adjusted indirect comparison (MAIC) and calibration estimation, proposing a new standard error estimator and comparing methods through simulation for improved indirect comparison analysis.

Contribution

It establishes the link between MAIC and calibration estimation, introduces a model-independent SE estimator, and compares various methods via simulation.

Findings

Entropy balancing is equivalent to MAIC.

The proposed SE estimator performs well in simulations.

Calibration methods vary in accuracy depending on scenarios.

Abstract

Indirect comparisons have been increasingly used to compare data from different sources such as clinical trials and observational data in, e.g., a disease registry. To adjust for population differences between data sources, matching-adjusted indirect comparison (MAIC) has been used in several applications including health technology assessment and drug regulatory submissions. In fact, MAIC can be considered as a special case of a range of methods known as calibration estimation in survey sampling. However, to our best knowledge, this connection has not been examined in detail. This paper makes three contributions: 1. We examined this connection by comparing MAIC and a few commonly used calibration estimation methods, including the entropy balancing approach, which is equivalent to MAIC. 2. We considered the standard error (SE) estimation of the MAIC estimators and propose a model-independent SE estimator and examine its performance by simulation. 3. We conducted a simulation to compare these commonly used approaches to evaluate their performance in indirect comparison scenarios.