Simplifying fractional polynomials in Bayesian network meta-analysis via variable powers.

ABSTRACT: Aim: Fractional Polynomial (FP) models are widely used in survival analysis for health technology assessment and network meta-analysis (NMA). However, current implementations rely on a fixed set of pre-specified powers, which may constrain model flexibility, limit predictive performance and increase computational cost in Bayesian settings. This study introduces and evaluates a Bayesian FP modeling approach in which the powers are estimated as continuous parameters rather than fixed, aiming to simplify model selection and improve fit. Materials & methods: Second-order Bayesian FP models were implemented in STAN, allowing the time transformation powers (p1, p2) to be estimated from the data. Model performance was evaluated across three oncology NMA datasets; in ad

SUBMITTER: Verhoek A 

PROVIDER: S-EPMC12711104 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature