Bayesian federated inference for estimating statistical models based on non-shared multicenter data sets.
Stat Med
; 43(12): 2421-2438, 2024 May 30.
Article
en En
| MEDLINE
| ID: mdl-38589978
ABSTRACT
Identifying predictive factors for an outcome of interest via a multivariable analysis is often difficult when the data set is small. Combining data from different medical centers into a single (larger) database would alleviate this problem, but is in practice challenging due to regulatory and logistic problems. Federated learning (FL) is a machine learning approach that aims to construct from local inferences in separate data centers what would have been inferred had the data sets been merged. It seeks to harvest the statistical power of larger data sets without actually creating them. The FL strategy is not always efficient and precise. Therefore, in this paper we refine and implement an alternative Bayesian federated inference (BFI) framework for multicenter data with the same aim as FL. The BFI framework is designed to cope with small data sets by inferring locally not only the optimal parameter values, but also additional features of the posterior parameter distribution, capturing information beyond what is used in FL. BFI has the additional benefit that a single inference cycle across the centers is sufficient, whereas FL needs multiple cycles. We quantify the performance of the proposed methodology on simulated and real life data.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
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Estudios Multicéntricos como Asunto
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Teorema de Bayes
Límite:
Humans
Idioma:
En
Año:
2024
Tipo del documento:
Article