Causal Perspectives on Network Meta-Analysis

2026-07-10

AI Summary

Traditional network meta-analysis often lacks a clearly defined causal target population, rendering effect estimates difficult to interpret causally. This work proposes a novel framework oriented toward causal estimands: it begins by explicitly specifying the target population and sources of heterogeneity, which naturally leads to an arm-based aggregation approach. For the first time, this method systematically integrates principles from causal inference into network meta-analysis, positioning arm-level aggregation as a necessary consequence of causal identification rather than a modeling choice, and eliminating reliance on the structure of the treatment network. A unified causal model based on aggregate data jointly incorporates effect modifiers and central effects to enable precise causal effect estimation. Numerical experiments demonstrate that the proposed estimator can yield substantially different conclusions from conventional methods in specific scenarios, thereby altering evidence interpretation.

Abstract

Pairwise and network meta-analyses occupy the highest tier of evidence-based medicine and routinely inform clinical guidelines and healthcare decision-making. Current approaches typically aggregate study-level treatment effects to obtain an overall estimate. We argue that the causal estimand should come first, with the aggregation derived only afterwards: the target population and the relevant sources of between-study heterogeneity should be explicitly defined before deriving the aggregation required for identification. This shift in perspective fundamentally changes both the estimands and the methodology. We develop a unified causal framework for pairwise and network meta-analysis based on aggregate data. By defining treatment effects with respect to a clinically meaningful target population, for example, the average population represented by the contributing trials, and accounting for heterogeneity induced by treatment-effect modifiers and center effects, we show that identification naturally leads to arm-level aggregation. In the network setting, this causal formulation departs fundamentally from the conventional contrast-based paradigm: arm-level aggregation emerges from the causal formulation rather than from a modeling choice, and treatment effects are identified without relying on the treatment network itself. This perspective provides an additional conceptual argument in the long-standing contrast-based versus arm-based debate. Numerical studies show that the proposed estimators target well-defined causal effects, whereas the causal interpretation of conventional approaches remains unclear. Although both approaches often produce similar estimates, we identify settings in which they diverge, with potentially important implications for the interpretation of meta-analytic evidence.

Problem

• causal inference
• network meta-analysis
• heterogeneity
• target population
• causal estimand

Innovation

• causal inference
• network meta-analysis
• arm-level aggregation
• treatment-effect modifiers
• target population