Group, Subgroup, and Person-Specific Symptom Associations in Individuals at Different Levels of Risk for Psychosis: A Combination of Theory-based and Data-driven Approaches

Abstract

Abstract Introduction Dynamics between symptoms may reveal insights into mechanisms underlying the development of psychosis. We combined a top-down (theory-based) and bottom-up (data-driven) approach to examine which symptom dynamics arise on group-level, on subgroup levels, and on individual levels in early clinical stages. We compared data-driven subgroups to theory-based subgroups, and explored how the data-driven subgroups differed from each other. Methods Data came from N = 96 individuals at risk for psychosis divided over four subgroups (n1 = 25, n2 = 27, n3 = 24, n4 = 20). Each subsequent subgroup represented a higher risk for psychosis (clinical stages 0-1b). All individuals completed 90 days of daily diaries, totaling 8640 observations. Confirmatory Subgrouping Group Iterative Multiple Model Estimation (CS-GIMME) and subgrouping (S-)-GIMME were used to examine group-level associations, respectively, theory-based and data-driven subgroups associations, and individual-specific associations between daily reports of depression, anxiety, stress, irritation, psychosis, and confidence. Results One contemporaneous group path between depression and confidence was identified. CS-GIMME identified several subgroup-specific paths and some paths that overlapped with other subgroups. S-GIMME identified two data-driven subgroups, with one subgroup reporting more psychopathology and lower social functioning. This subgroup contained most individuals from the higher stages and those with more severe psychopathology from the lower stages, and shared more connections between symptoms. Discussion Although subgroup-specific paths were recovered, no clear ordering of symptom patterns was found between different early clinical stages. Theory-based subgrouping distinguished individuals based on psychotic severity, whereas data-driven subgrouping distinguished individuals based on overall psychopathological severity. Future work should compare the predictive value of both methods.