Table Info

Table 1.

Predictors per hierarchical block modeling approach

Sequential modeling	Predictors per each model
Base model 1: demographics and MS covariates	Age, gender, education, MS subtype, DMT^a, pain medication count^b
Model 2 adds childhood stressors	Base model 1 + childhood stressor count, childhood stressor severity
Model 3 adds adult stressors	Model 2 + adult stressor count, adult stressor severity

^a DMTs not included in psychiatric morbidity analysis; ^b pain medication count only included for pain interference and psychiatric morbidity analyses

Declarations

Acknowledgments

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of Duke CTSI or the VA.

Author contributions

CSP: Conceptualization, Formal analysis, Writing—original draft. TJB: Conceptualization, Resources, Writing—review & editing. RPS: Conceptualization, Formal analysis, Writing—review & editing. CMC: Conceptualization, Writing—review & editing. AW: Writing—review & editing. SAS: Conceptualization, Supervision, Writing—review & editing.

Conflicts of interest

The authors have no conflicts of interest to declare.

Ethical approval

This study was approved by the University of Michigan IRB (HUM00200716).

Consent to participate

Participants read informed consent materials during the screening process and agreed to an implied consent statement to proceed with the study.

Consent to publication

Not applicable.

Availability of data and materials

Data is available upon request to corresponding author or Harvard Dataverse repository.

Funding

CSP was supported by NIH/NINR grant [T32NR016914], “Complexity: Innovations for Promoting Health and Safety”, Rackham Graduate School, Duke Clinical and Translational Science Institute (CTSI), and Durham VA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

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