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, Giovanni Poli Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence , Florence, 50134 , Italy Corresponding author: Giovanni Poli, Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, Florence 50134, Italy (giovanni.poli@unifi.it). Search for other works by this author on: Oxford Academic Elena Fountzilas Department of Medical Oncology, St Luke’s Clinic, Thessalonik , 55236 , Greece Search for other works by this author on: Oxford Academic Apostolia-Maria Tsimeridou Department of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center , Houston, TX 77030 , United States Search for other works by this author on: Oxford Academic Peter Müller Department of Statistics and Data Science, University of Texas at Austin , Austin, TX 78705 , United States Search for other works by this author on: Oxford Academic
https://orcid.org/0000-0001-9949-0422 Biometrics, Volume 80, Issue 4, December 2024, ujae136, https://doi.org/10.1093/biomtc/ujae136
Published:
30 November 2024
Article history
Received:
12 December 2023
Revision received:
03 October 2024
Accepted:
29 October 2024
Published:
30 November 2024
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Giovanni Poli, Elena Fountzilas, Apostolia-Maria Tsimeridou, Peter Müller, A multivariate Polya tree model for meta-analysis with event-time distributions, Biometrics, Volume 80, Issue 4, December 2024, ujae136, https://doi.org/10.1093/biomtc/ujae136
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ABSTRACT
We develop a nonparametric Bayesian prior for a family of random probability measures by extending the Polya tree (|$\mbox{PT}$|) prior to a joint prior for a set of probability measures |$G_1,\dots ,G_n$|, suitable for meta-analysis with event-time outcomes. In the application to meta-analysis, |$G_i$| is the event-time distribution specific to study |$i$|. The proposed model defines a regression on study-specific covariates by introducing increased correlation for any pair of studies with similar characteristics. The desired multivariate |$\mbox{PT}$| model is constructed by introducing a hierarchical prior on the conditional splitting probabilities in the |$\mbox{PT}$| construction for each of the |$G_i$|. The hierarchical prior replaces the independent beta priors for the splitting probability in the PT construction with a Gaussian process prior for corresponding (logit) splitting probabilities across all studies. The Gaussian process is indexed by study-specific covariates, introducing the desired dependence with increased correlation for similar studies. The main feature of the proposed construction is (conditionally) conjugate posterior updating with commonly reported inference summaries for event-time data. The construction is motivated by a meta-analysis over cancer immunotherapy studies.
Gaussian process, nonparametric inference, survival analysis
© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
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Biometric Methodology
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