Bayesian generalized method of moments applied to pseudo-observations in survival analysis

Léa Orsini (), Caroline Brard (), Emmanuel Lesaffre (), Guosheng Yin (), David Dejardin () and Gwénaël Le Teuff ()
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Léa Orsini: University Paris-Saclay
Caroline Brard: Ipsen Innovation
Emmanuel Lesaffre: KU-Leuven
Guosheng Yin: The University of Hong Kong
David Dejardin: F. Hoffmann-La Roche AG
Gwénaël Le Teuff: University Paris-Saclay

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2025, vol. 31, issue 4, No 11, 970-993

Abstract: Abstract Bayesian inference for survival regression modeling offers numerous advantages, especially for decision-making and external data borrowing, but demands the specification of the baseline hazard function, which may be a challenging task. We propose an alternative approach that does not need the specification of this function. Our approach combines pseudo-observations to convert censored data into longitudinal data with the generalized method of moments (GMM) to estimate the parameters of interest from the survival function directly. GMM may be viewed as an extension of the generalized estimating equations (GEE) currently used for frequentist pseudo-observations analysis and can be extended to the Bayesian framework using a pseudo-likelihood function. We assessed the behavior of the frequentist and Bayesian GMM in the new context of analyzing pseudo-observations. We compared their performances to the Cox, GEE, and Bayesian piecewise exponential models through a simulation study of two-arm randomized clinical trials. Frequentist and Bayesian GMMs gave valid inferences with similar performances compared to the three benchmark methods, except for small sample sizes and high censoring rates. For illustration, three post-hoc efficacy analyses were performed on randomized clinical trials involving patients with Ewing Sarcoma, producing results similar to those of the benchmark methods. Through a simple application of estimating hazard ratios, these findings confirm the effectiveness of this new Bayesian approach based on pseudo-observations and the generalized method of moments. This offers new insights on using pseudo-observations for Bayesian survival analysis.

Keywords: Bayesian analysis; Generalized method of moments; Pseudo-observations; Survival analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10985-025-09670-1

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