Bayesian Pathway Analysis for Complex Interactions.
Am J Epidemiol
; 189(12): 1610-1622, 2020 12 01.
Article
em En
| MEDLINE
| ID: mdl-32639515
ABSTRACT
Modern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex data analysis. ALPS uses prior information about pathways in concert with empirical data to identify and quantify complex interactions within networks of factors that mediate an association between an exposure and an outcome. We illustrate ALPS through application to a complex gene-drug interaction analysis in the Predictors of Breast Cancer Recurrence (ProBe CaRe) Study, a Danish cohort study of premenopausal breast cancer patients (2002-2011), for which conventional analyses severely limit the quality of inference.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Teorema de Bayes
/
Resistencia a Medicamentos Antineoplásicos
/
Testes Farmacogenômicos
Tipo de estudo:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Female
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Humans
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article