Network Meta Analysis of Mean Survival

TL;DR

This paper introduces a Bayesian network meta-analysis framework for directly estimating and extrapolating mean survival times across treatments, addressing limitations of surrogate measures like hazard ratios.

Contribution

It develops a novel Bayesian approach using flexible poly-hazard and M-spline models for stable survival extrapolation, improving decision-making in treatment comparisons.

Findings

Effective extrapolation of survival curves using the proposed models

Enhanced accuracy in treatment ranking based on mean survival

Application to real datasets demonstrates practical utility

Abstract

Decisions based upon pairwise comparisons of multiple treatments are naturally performed in terms of the mean survival of the selected study arms or functions thereof. However, synthesis of treatment comparisons is usually performed on surrogates of the mean survival, such as hazard ratios or restricted mean survival times. Thus, network meta-analysis techniques may suffer from the limitations of these approaches, such as incorrect proportional hazards assumption or short-term follow-up periods. We propose a Bayesian framework for the network meta-analysis of the main outcome informing the decision, the mean survival of a treatment. Its derivation involves extrapolation of the observed survival curves. We use methods for stable extrapolation that integrate long term evidence based upon mortality projections. Extrapolations are performed using flexible poly-hazard parametric models and M-spline-based methods. We assess the computational and statistical efficiency of different techniques using a simulation study and apply the developed methods to two real data sets. The proposed method is formulated within a decision theoretic framework for cost-effectiveness analyses, where the `best' treatment is to be selected and incorporating the associated cost information is straightforward.