Aligning Estimands to Strengthen the Credibility of Network Meta-Analysis: Implications for Indirect Treatment Comparisons.

Network meta-analysis (NMA) has become a cornerstone of evidence synthesis, enabling simultaneous comparison of multiple interventions using both direct and indirect evidence. However, recent developments in the estimand framework, formalized in ICH E9 (R1), have highlighted conceptual ambiguities in how treatment effects are defined, interpreted, and synthesized in complex evidence networks. We review the core components of the estimand framework and evaluate how each is handled in standard NMA practice. Particular attention is given to heterogeneity in strategies for handing intercurrent events across trials, including treatment switching and non-adherence, and to the reliance of NMA on transitivity and consistency assumptions when underlying estimands may not be aligned. We identify several key challenges in the estimation for NMA. First, individual trials within a network may target different estimands, despite nominally similar comparisons, leading to incoherent synthesis targets. Second, NMA models may implicitly combine estimands aligned with different intercurrent event strategies, complicating causal interpretation of NMA estimates. Failure to align estimands across trials and comparisons can compromise transitivity, distort treatment rankings, and mislead decision-making. We argue that incorporating the estimand framework formally into NMA design, analysis, and reporting is critical for improving the transparency, interpretability, and credibility of evidence synthesized by NMA.