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Comparison of regmed and BayesNetty for exploring causal models with many variables

Lookup NU author(s): Dr Richard Howey, Professor Heather Cordell

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2023 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.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.

Publication metadata

Author(s): Howey R, Cordell HJ

Publication type: Article

Publication status: Published

Journal: Genetic Epidemiology

Year: 2023

Volume: 47

Issue: 7

Pages: 496-502

Print publication date: 01/10/2023

Online publication date: 27/06/2023

Acceptance date: 02/06/2023

Date deposited: 11/07/2023

ISSN (print): 0741-0395

ISSN (electronic): 1098-2272

Publisher: John Wiley and Sons Inc

URL: https://doi.org/10.1002/gepi.22532

DOI: 10.1002/gepi.22532

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