Multi‐state network meta‐analysis of progression and survival data

Abstract

Summary Multiple randomized controlled trials, each comparing a subset of competing interventions, can be synthesized by means of a network meta‐analysis to estimate relative treatment effects between all interventions in the evidence base. Here we focus on estimating relative treatment effects for time‐to‐event outcomes. Cancer treatment effectiveness is frequently quantified by analyzing overall survival (OS) and progression‐free survival (PFS). We introduce a method for the joint network meta‐analysis of PFS and OS that is based on a time‐inhomogeneous tri‐state (stable, progression, and death) Markov model where time‐varying transition rates and relative treatment effects are modeled with parametric survival functions or fractional polynomials. The data needed to run these analyses can be extracted directly from published survival curves. We demonstrate use by applying the methodology to a network of trials for the treatment of non‐small‐cell lung cancer. The proposed approach allows the joint synthesis of OS and PFS, relaxes the proportional hazards assumption, extends to a network of more than two treatments, and simplifies the parameterization of decision and cost‐effectiveness analyses.

survextrap: a package for flexible and transparent survival extrapolation

“…See, e.g. [ 44 ] for a comparison of flexible parametric frameworks for multi-state models, and [ 45 ] for network meta-analysis of survival data with multiple outcomes. The ideas described in this paper would enable any of these previous methods to be strengthened by including background information from external data.…”