On Multi-Armed Bandit Designs for Dose-Finding Trials

Aziz, Maryam; Kaufmann, Emilie; Riviere, Marie-Karelle

Document type :

Compte-rendu et recension critique d'ouvrage

Title :

On Multi-Armed Bandit Designs for Dose-Finding Trials

Author(s) :

Aziz, Maryam [Auteur]
SPOTIFY [London]
Kaufmann, Emilie [Auteur]

Scool [Scool]
Riviere, Marie-Karelle [Auteur]
SANOFI Recherche

Journal title :

Journal of Machine Learning Research

Publisher :

Microtome Publishing

Publication date :

2021-01

ISSN :

1532-4435

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

We study the problem of finding the optimal dosage in early stage clinical trials through the multi-armed bandit lens. We advocate the use of the Thompson Sampling principle, a flexible algorithm that can accommodate ...
Show more >We study the problem of finding the optimal dosage in early stage clinical trials through the multi-armed bandit lens. We advocate the use of the Thompson Sampling principle, a flexible algorithm that can accommodate different types of monotonicity assumptions on the toxicity and efficacy of the doses. For the simplest version of Thompson Sampling, based on a uniform prior distribution for each dose, we provide finite-time upper bounds on the number of sub-optimal dose selections, which is unprecedented for dose-finding algorithms. Through a large simulation study, we then show that variants of Thompson Sampling based on more sophisticated prior distributions outperform state-of-the-art dose identification algorithms in different types of dose-finding studies that occur in phase I or phase I/II trials.Show less >

Language :

Anglais

Popular science :

Non

ANR Project :

Au delà de l'apprentissage séquentiel pour de meilleures prises de décisions
BANDITS MANCHOTS POUR SIGNAUX NON-STATIONNAIRES ET STRUCTURES

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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On Multi-Armed Bandit Designs for Dose-Finding ...

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