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Journal Articles Studies in Health Technology and Informatics Year : 2021

Ontological Models Supporting Covariates Selection in Observational Studies

Ontological Models Supporting Covariates Selection in Observational Studies.

Abstract

In the context of causal inference, biostatisticians use causal diagrams to select covariates in order to build multivariate models. These diagrams represent datasets variables and their relations but have some limitations (representing interactions, bidirectional causal relations). The MetBrAYN project aims at building an ontological-based process to tackle these issues. The knowledge acquired by the biostatistician during a methodological consultation for a research question will be represented in a general ontology. In order to aggregate various forms of knowledge the ontology will act as a wrapper. Ontology-based causal diagrams will be semi-automatically built. Founded on inference rules, the global system will help biostatisticians to curate it and to visualize recommended covariates for their research question.

Dates and versions

hal-03243046 , version 1 (31-05-2021)

Identifiers

Cite

Thibaut Pressat Laffouilhère, Julien Grosjean, Jacques Bénichou, Stefan Darmoni, Lina F. Soualmia. Ontological Models Supporting Covariates Selection in Observational Studies. Studies in Health Technology and Informatics, 2021, 281, pp.1095-1096. ⟨10.3233/SHTI210361⟩. ⟨hal-03243046⟩
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