One-step parametric network meta-analysis models using the exact likelihood that allow for time-varying treatment effects.

ABSTRACT: The importance of network meta-analysis (NMA) methods for time-to-event (TTE) that do not rely on the proportional hazard (PH) assumption is increasingly recognized in oncology, where clinical trials evaluating new interventions versus standard comparators often violate this assumption. However, existing NMA methods that allow for time-varying treatment effects do not directly leverage individual events and censor times that can be reconstructed from Kaplan-Meier curves, which may be more accurate than discrete hazards. They are also challenging to implement given reparameterizations that rely on discrete hazards. Additionally, two-step methods require assumptions regarding within-study normality and variance. We propose a one-step fully Bayesian parametric individual patient data (IPD)-NM

SUBMITTER: Campbell H 

PROVIDER: S-EPMC12527511 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature