# Using Symptom Network Models to Translate Theory to Clinical Applications

> **NIH NIH F31** · UNIVERSITY OF MISSOURI-COLUMBIA · 2021 · $37,042

## Abstract

PROJECT SUMMARY/ABSTRACT
Broad/Long Term Objectives: The proposed research has two broad goals: to improve diagnostic
conceptualization of AUD by adopting a symptom-focused approach that is more consistent with contemporary
theoretical models of addiction (e.g., allostasis); and to ascertain the extent to which this approach can be
translated into clinical applications.
Specific Aims: The aims of the proposed project are to: characterize how individual AUD symptoms uniquely
predict the onset, persistence, and recurrence (course) of other symptoms; resolve diagnostic heterogeneity and
improve classification by examining symptom structure in a priori and empirically derived subgroups; and analyze
the extent to which different symptoms and symptom subgroups predict clinical outcomes across different forms
of treatment.
Research Design and Method: The project will consist of secondary data analysis using both waves of the
National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), the COMBINE study, and Project
MATCH. In NESARC, symptom network modelling (SNM) will be used to identify key symptoms predicting the
course of the other symptoms and identify core features of symptom subgroups. The project will also compare
a priori subgroups based on etiologic (e.g., Conduct Disorder, heavy drinking patterns) risk factors and
subgroups of symptom profiles empirically derived via cluster analysis. In MATCH and COMBINE, general mixed
models and group factor analysis will be applied analyze the interaction between symptom profile, treatment
condition, and treatment outcomes.
Significance: This project will advance the understanding of how AUD diagnostic criteria reflect the endogenous
processes proposed by modern addiction theories, help resolve diagnostic heterogeneity, and improve
diagnostic validity. Additionally, the results of the project will allow for more effective tailoring and implementation
of focused assessment and identification of potential targets for treatment.
Training Plan and Environment: The training plan is designed to provide the applicant with quantitative,
substantive, and practical training to facilitate a successful career as an independent investigator. The applicant
will receive training in advanced multivariate statistics, application of theoretical models of addiction to clinical
outcomes, open sciences practices, and general scientific writing. Training will take place at the University of
Missouri’s Department of Psychological Sciences, which has an outstanding addiction training program funded
by an NIAAA training grant (T32 AA013526; PI: Kenneth Sher). The mentoring team consists of experts in
quantitative (Dr. Steinley, Dr. Wood) and substantive (Dr. Sher, Dr. Witkiewitz) research on AUD. Members of
the team have a long collegial history, providing a synergistic training experience for the applicant.

## Key facts

- **NIH application ID:** 10387871
- **Project number:** 1F31AA029949-01
- **Recipient organization:** UNIVERSITY OF MISSOURI-COLUMBIA
- **Principal Investigator:** William E Conlin
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $37,042
- **Award type:** 1
- **Project period:** 2022-03-01 → 2024-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10387871

## Citation

> US National Institutes of Health, RePORTER application 10387871, Using Symptom Network Models to Translate Theory to Clinical Applications (1F31AA029949-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10387871. Licensed CC0.

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