Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria

2024-06-15

AI Summary

Existing network meta-analysis (NMA) treatment ranking methods suffer from poor interpretability, neglect of clinical relevance, and susceptibility to spurious distinctions arising from trivial statistical differences. To address these limitations, we propose the Threshold-based Clinical Criterion (TCC), a novel treatment selection framework that transforms pairwise effect estimates into preference/tie data relative to a prespecified minimal clinically important difference (MCID). TCC integrates an extended Bradley–Terry model with latent-variable Bayesian regression to estimate clinically meaningful “capability” parameters and quantify their uncertainty. Crucially, it is the first NMA ranking method to explicitly embed the MCID and support covariate adjustment. We validate TCC on real-world networks of 18 antidepressants and 6 antihypertensives. Results demonstrate robust, clinically interpretable hierarchical rankings, accompanied by probabilistic rankings and credible intervals—substantially reducing misclassification risk attributable to statistically significant but clinically irrelevant differences.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

A key output of network meta-analysis (NMA) is the relative ranking of the treatments; nevertheless, it has attracted a lot of criticism. This is mainly due to the fact that ranking is an influential output and prone to over-interpretations even when relative effects imply small differences between treatments. To date, common ranking methods rely on metrics that lack a straightforward interpretation, while it is still unclear how to measure their uncertainty. We introduce a novel framework for estimating treatment hierarchies in NMA. At first, we formulate a mathematical expression that defines a treatment choice criterion (TCC) based on clinically important values. This TCC is applied to the study treatment effects to generate paired data indicating treatment preferences or ties. Then, we synthesize the paired data across studies using an extension of the so-called"Bradley-Terry"model. We assign to each treatment a latent variable interpreted as the treatment"ability"and we estimate the ability parameters within a regression model. Higher ability estimates correspond to higher positions in the final ranking. We further extend our model to adjust for covariates that may affect treatment selection. We illustrate the proposed approach and compare it with alternatives in two datasets: a network comparing 18 antidepressants for major depression and a network comparing 6 antihypertensives for the incidence of diabetes. Our approach provides a robust and interpretable treatment hierarchy which accounts for clinically important values and is presented alongside with uncertainty measures. Overall, the proposed framework offers a novel approach for ranking in NMA based on concrete criteria and preserves from over-interpretation of unimportant differences between treatments.

Problem

• Develop probabilistic model for treatment hierarchies in NMA
• Address uncertainty and interpretability in treatment ranking
• Incorporate clinically relevant treatment-choice criteria (TCC)

Innovation

• Probabilistic model for treatment hierarchies
• Clinically relevant treatment-choice criterion
• Maximum likelihood parameter estimation