On matching-adjusted indirect comparison and calibration estimation

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.