Can We Trust PAICs in Rare Diseases? Methodological Challenges and Limitations

Population-adjusted indirect comparisons (PAICs), including Matching-Adjusted Indirect Comparison and Simulated Treatment Comparison, are increasingly used to inform health technology assessments. These methods offer a pragmatic approach to generating comparative evidence between treatments when head-to-head trial data are unavailable and standard indirect treatment comparison methods are unfeasible. In rare diseases, however, PAICs often face substantial methodological challenges arising from small sample sizes, limited covariate overlap, and the frequent use of unanchored comparisons that rely on unverifiable assumptions. These limitations can lead to unstable estimates, reduced precision, and bias that may undermine the reliability of findings. Methodological refinements-such as optimized weighting, Bayesian approaches, and doubly robust estimators-provide some improvements but do not resolve these fundamental issues. Current European Joint Clinical Assessment guidance recommends that anchored PAICs be applied with great caution, while unanchored PAICs are considered highly problematic, and other methods should be used instead. We argue that PAICs can play a supportive role within a multidimensional and deliberative HTA process, contributing to comparative assessment alongside other evidence sources when available data are limited. However, their results require careful interpretation and transparent communication of uncertainty. Future research should prioritize the further development of formal frameworks to quantify bias and systematically assess robustness, thereby preventing overstatement of the credibility of PAIC-derived evidence in rare disease contexts.