Subgrouping with Chain Graphical VAR Models

Abstract

Recent years have seen the emergence of an “idio-thetic” class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel “idio-thetic” model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach—the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

Keywords:

• Community detection
• dynamic network modeling
• vector autoregression
• psychopathology

Article information

Conflict of interest disclosures: Each author signed a form for disclosure of potential conflicts of interest. No authors reported any financial or other conflicts of interest in relation to the work described.

Ethical principles: The authors affirm having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treat- ment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual partici- pants cannot be identified in reported results or from publicly available original or archival data.

Funding: This research reported in this publication was supported by the National Institutes of Health under grants U24AA027684 and UL1 TR002014; and National Science Foundation grant IGE-1806874 alongside grants UH3 OD023389, 5 R01 HD097189-04, U2C OD023375, and 5 UL1TR002014-06.

Role of funders/sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Acknowledgements: The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors’ institution or the respective funding agencies is not intended and should not be inferred.