PMID- 32627167
OWN - NLM
STAT- MEDLINE
DCOM- 20211025
LR  - 20211025
IS  - 1541-0420 (Electronic)
IS  - 0006-341X (Print)
IS  - 0006-341X (Linking)
VI  - 77
IP  - 2
DP  - 2021 Jun
TI  - Structural factor equation models for causal network construction via directed 
      acyclic mixed graphs.
PG  - 573-586
LID - 10.1111/biom.13322 [doi]
AB  - Directed acyclic mixed graphs (DAMGs) provide a useful representation of network 
      topology with both directed and undirected edges subject to the restriction of no 
      directed cycles in the graph. This graphical framework may arise in many 
      biomedical studies, for example, when a directed acyclic graph (DAG) of interest 
      is contaminated with undirected edges induced by some unobserved confounding 
      factors (eg, unmeasured environmental factors). Directed edges in a DAG are 
      widely used to evaluate causal relationships among variables in a network, but 
      detecting them is challenging when the underlying causality is obscured by some 
      shared latent factors. The objective of this paper is to develop an effective 
      structural equation model (SEM) method to extract reliable causal relationships 
      from a DAMG. The proposed approach, termed structural factor equation model 
      (SFEM), uses the SEM to capture the network topology of the DAG while accounting 
      for the undirected edges in the graph with a factor analysis model. The latent 
      factors in the SFEM enable the identification and removal of undirected edges, 
      leading to a simpler and more interpretable causal network. The proposed method 
      is evaluated and compared to existing methods through extensive simulation 
      studies, and illustrated through the construction of gene regulatory networks 
      related to breast cancer.
CI  - (c) 2020 The International Biometric Society.
FAU - Zhou, Yan
AU  - Zhou Y
AD  - Gilead Sciences, Foster City, California.
FAU - Song, Peter X-K
AU  - Song PX
AUID- ORCID: 0000-0001-7881-7182
AD  - Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.
FAU - Wen, Xiaoquan
AU  - Wen X
AD  - Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.
LA  - eng
GR  - R01 ES024732/ES/NIEHS NIH HHS/United States
GR  - R35 GM138121/GM/NIGMS NIH HHS/United States
GR  - 1181734/National Science Foundation, Division of Mathematical Sciences/
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20200718
PL  - England
TA  - Biometrics
JT  - Biometrics
JID - 0370625
SB  - IM
MH  - Causality
MH  - Factor Analysis, Statistical
MH  - *Models, Theoretical
MH  - *Research Design
PMC - PMC8240035
MID - NIHMS1717175
OTO - NOTNLM
OT  - directed acyclic graph
OT  - factor analysis model
OT  - network data
OT  - regularization
OT  - semi-Markov model
EDAT- 2020/07/07 06:00
MHDA- 2021/10/26 06:00
PMCR- 2021/06/29
CRDT- 2020/07/07 06:00
PHST- 2018/08/08 00:00 [received]
PHST- 2020/05/29 00:00 [accepted]
PHST- 2020/07/07 06:00 [pubmed]
PHST- 2021/10/26 06:00 [medline]
PHST- 2020/07/07 06:00 [entrez]
PHST- 2021/06/29 00:00 [pmc-release]
AID - 10.1111/biom.13322 [doi]
PST - ppublish
SO  - Biometrics. 2021 Jun;77(2):573-586. doi: 10.1111/biom.13322. Epub 2020 Jul 18.