Network Meta-Analysis of Time-to-Event Endpoints with Individual Participant Data using Restricted Mean Survival Time Regression

TL;DR

This paper introduces advanced network meta-analysis models using restricted mean survival time with individual data, enabling better treatment comparison and subgroup analysis in time-to-event studies.

Contribution

It develops novel RMST-based NMA models that incorporate individual data, allowing for treatment effect moderation and subgroup insights.

Findings

Models effectively analyze treatment effects with RMST

Simulation studies validate model performance

Real data example demonstrates practical utility

Abstract

Restricted mean survival time (RMST) models have gained popularity when analyzing time-to-event outcomes because RMST models offer more straightforward interpretations of treatment effects with fewer assumptions than hazard ratios commonly estimated from Cox models. However, few network meta-analysis (NMA) methods have been developed using RMST. In this paper, we propose advanced RMST NMA models when individual participant data are available. Our models allow us to study treatment effect moderation and provide comprehensive understanding about comparative effectiveness of treatments and subgroup effects. An extensive simulation study and a real data example about treatments for patients with atrial fibrillation are presented.