Comparison of regmed and BayesNetty for exploring causal models with many variables.
Bayesian networks
causal inference
mediation
Journal
Genetic epidemiology
ISSN: 1098-2272
Titre abrégé: Genet Epidemiol
Pays: United States
ID NLM: 8411723
Informations de publication
Date de publication:
10 2023
10 2023
Historique:
revised:
13
04
2023
received:
07
12
2022
accepted:
02
06
2023
medline:
11
10
2023
pubmed:
27
6
2023
entrez:
27
6
2023
Statut:
ppublish
Résumé
Here we compare a recently proposed method and software package, regmed, with our own previously developed package, BayesNetty, designed to allow exploratory analysis of complex causal relationships between biological variables. We find that regmed generally has poorer recall but much better precision than BayesNetty. This is perhaps not too surprising as regmed is specifically designed for use with high-dimensional data. BayesNetty is found to be more sensitive to the resulting multiple testing problem encountered in these circumstances. However, as regmed is not designed to handle missing data, its performance is severely affected when missing data is present, whereas the performance of BayesNetty is only slightly affected. The performance of regmed can be rescued in this situation by first using BayesNetty to impute the missing data, and then applying regmed to the resulting "filled-in" data set.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
496-502Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 219424/Z/19/Z
Pays : United Kingdom
Informations de copyright
© 2023 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.
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