Format

Send to

Choose Destination
Sci Rep. 2016 May 5;6:25156. doi: 10.1038/srep25156.

Learning Bayesian Networks from Correlated Data.

Author information

1
Oregon State University, College of Public Health and Human Sciences, Corvallis, 97331, USA.
2
Boston University, Department of Medicine, Boston, 02118, USA.
3
Harvard Medical School, Department of Medicine, Boston, 02115, USA.
4
Boston University, Department of Biostatistics, Boston, 02118, USA.

Abstract

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

PMID:
27146517
PMCID:
PMC4857179
DOI:
10.1038/srep25156
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center