Presentation (CTI)

OBJECTIVES: In the absence of head-to-head trials, population-adjusted indirect comparisons (PAICs) are critical for assessing the comparative effectiveness of healthcare interventions. However, significant methodological developments in recent years have led to a complex landscape of available methods. Considerable uncertainty persists among analysts and health technology assessment (HTA) bodies regarding the most appropriate PAIC method for a given evidence scenario. We aim to provide a clear roadmap for selecting an appropriate PAIC method, thereby enhancing the consistency, transparency, and validity of such analyses.
METHODS: We provide a comprehensive overview of the key PAIC methods, including Matching-Adjusted Indirect Comparison (MAIC), Simulated Treatment Comparison (STC) and Multi-Level Network Meta-Regression (ML-NMR). The strengths and limitations of each method will be compared, focusing on underlying assumptions and data requirements (individual patient data vs. aggregate data).
RESULTS: The primary output is a step-by-step decision framework designed to guide researchers and HTA professionals in the transparent selection of a PAIC method. This framework will prompt users to consider critical factors such as the structure of the evidence network, the availability and granularity of data, the distribution of effect modifiers, the degree of covariate overlap, and the specific research question. An accompanying flow diagram will visually guide users through the decision pathways, highlighting how the strengths of one method may mitigate the limitations of another in specific scenarios. Furthermore, we will provide recommendations for the transparent reporting of the chosen methodology and the justification for its selection.
CONCLUSIONS: The proposed decision framework offers a structured and transparent approach to navigating the complexities of modern PAIC methods. By providing a clear roadmap, this work aims to reduce ambiguity in methods selection, improve the quality and consistency of HTA submissions, and ultimately foster greater confidence in the resulting comparative effectiveness evidence.