Dirichlet process mixtures under affine transformations of the data

Arbel, Julyan; Corradin, Riccardo; Nipoti, Bernardo

doi:10.1007/s00180-020-01013-y

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…This problem is often addressed with an empirical Bayes approach (e.g. Arbel et al, 2021). Since in our case the random partition distribution acts as prior on a random effect, rather than on data, this approach is not feasible.…”

Section: Discussionmentioning

confidence: 99%

A Bayesian nonparametric approach to personalized treatment selection

Pedone¹,

Argiento²,

Stingo³

2022

Preprint

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Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of therapeutic strategies is better suited for a new patient. The proposed method is fully model-based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the normalized generalized gamma process. The method performs particularly well in scenarios characterized by large heterogeneity of the predictive covariates, in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model-based, the approach allows estimating clusters' specific random effects and then identify patients that are more likely to benefit from personalized treatment.

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Section: Discussionmentioning

confidence: 99%

A Bayesian nonparametric approach to personalized treatment selection

Pedone¹,

Argiento²,

Stingo³

2022

Preprint

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High‐Dimensional Bayesian Semiparametric Models for Small Samples: A Principled Approach to the Analysis of Cytokine Expression Data

Poli,

Argiento,

Amedei

et al. 2024

Biometrical J

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In laboratory medicine, due to the lack of sample availability and resources, measurements of many quantities of interest are commonly collected over a few samples, making statistical inference particularly challenging. In this context, several hypotheses can be tested, and studies are not often powered accordingly. We present a semiparametric Bayesian approach to effectively test multiple hypotheses applied to an experiment that aims to identify cytokines involved in Crohn's disease (CD) infection that may be ongoing in multiple tissues. We assume that the positive correlation commonly observed between cytokines is caused by latent groups of effects, which in turn result from a common cause. These clusters are effectively modeled through a Dirichlet Process (DP) that is one of the most popular choices as nonparametric prior in Bayesian statistics and has been proven to be a powerful tool for model‐based clustering. We use a spike–slab distribution as the base measure of the DP. The nonparametric part has been included in an additive model whose parametric component is a Bayesian hierarchical model. We include simulations that empirically demonstrate the effectiveness of the proposed testing procedure in settings that mimic our application's sample size and data structure. Our CD data analysis shows strong evidence of a cytokine gradient in the external intestinal tissue.

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