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

IF 1.7 4区医学 Q3 GENETICS & HEREDITY

Genetic Epidemiology Pub Date : 2023-06-27 DOI:10.1002/gepi.22532

Richard Howey, Heather J. Cordell

引用次数: 0

Abstract

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.

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regmed和贝叶斯网络在探索多变量因果模型方面的比较。

在这里，我们将最近提出的方法和软件包regmed与我们之前开发的包BayesNetty进行了比较，该包旨在对生物变量之间的复杂因果关系进行探索性分析。我们发现regmed通常比BayesNetty具有较差的召回率，但精度要好得多。这也许并不太令人惊讶，因为regmed是专门为高维数据而设计的。BayesNetty被发现对在这些情况下遇到的多重测试问题更敏感。然而，由于regmed不是为处理丢失的数据而设计的，因此当存在丢失的数据时，其性能会受到严重影响，而BayesNetty的性能只会受到轻微影响。在这种情况下，可以通过首先使用BayesNetty来估算丢失的数据，然后将regmed应用于生成的“填充”数据集来挽救regmed的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Genetic Epidemiology 医学-公共卫生、环境卫生与职业卫生

CiteScore

4.40

自引率

9.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed on the relative contribution of genetic and environmental factors to human disease as revealed by genetic, epidemiological, and biologic investigations. Genetic Epidemiology primarily publishes papers in statistical genetics, a research field that is primarily concerned with development of statistical, bioinformatical, and computational models for analyzing genetic data. Incorporation of underlying biology and population genetics into conceptual models is favored. The Journal seeks original articles comprising either applied research or innovative statistical, mathematical, computational, or genomic methodologies that advance studies in genetic epidemiology. Other types of reports are encouraged, such as letters to the editor, topic reviews, and perspectives from other fields of research that will likely enrich the field of genetic epidemiology.