Examining the Relationship Between Network-Based Subgroups and Treatment Outcomes Among Individuals With Eating...

Psychological treatment effects and response rates have largely plateaued over the past few decades. A potential answer to this problem is personalized treatment approaches that match treatment to a client's specific presenting concerns, increasing its precision and efficacy. Examining predictors and moderators of treatment outcome (who is likely to benefit from treatment, and which treatment) is one way to guide personalized decision making. The current study is a proof of concept for the clinical utility of network-based subgroups. We investigated the relationship between network-derived subgroups and treatment response using data from two clinical trials on eating disorders (N = 80). Subgroups were identified using subgrouping group iterative multiple model estimation (S-GIMME). Analyses of variance (ANOVAs) were used to compare changes in eating disorder symptom severity and clinical impairment among subgroups. We found three subgroups comprising 71 of the initial 80 participants: mean age = 34.4 (SD = 11.8), 87.3% cisgender women, 85.9% White non-Hispanic. Subgroups were differentiated by how shame and guilt were related in the network. The subgroup with a contemporaneous pathway from guilt to shame showed the least change in clinical impairment from pre- to posttreatment, F(2, 64) = 5.92, p = .004. Overall, our findings tentatively suggest that network-based subgroups may have utility as prognostic indicators in the context of eating disorders, though replication of present findings is warranted. Limitations included potentially unstable subgroups and use of mostly cisgender women and white samples.