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Background The clinical staging model states that psychosis develops through subsequent stages of illness severity. To better understand what drives illness progression, more extensive comparison across clinical stages is needed. The current paper presents an in‐depth characterization of individuals with different levels of risk for psychosis (i.e., different early clinical stages), using a multimethod approach of cross‐sectional assessments and daily diary reports. Methods Data came from the Mirorr study that includes N = 96 individuals, divided across four subgroups (n1 = 25, n2 = 27, n3 = 24, and n4 = 20). These subgroups, each with an increasing risk for psychosis, represent clinical stages 0‐1b. Cross‐sectional data and 90‐day daily diary data on psychopathology, well‐being, psychosocial functioning, risk and protective factors were statistically compared across subgroups (stages) and descriptively compared across domains and assessment methods. Results Psychopathology increased across subgroups, although not always linearly and nuanced differences were seen between assessment methods. Well‐being and functioning differed mostly between subgroup 1 and the other subgroups, suggesting differences between non‐clinical and clinical populations. Risk and protective factors differed mostly between the two highest and lowest subgroups, especially regarding need of social support and coping, suggesting differences between those with and without substantial psychotic experiences. Subgroup 4 (stage 1b) reported especially high levels of daily positive and negative psychotic experiences. Conclusions Risk for psychosis exists in larger contexts of mental health and factors of risk and protection that differ across stages and assessment methods. Taking a broad, multi‐method approach is an important next step to understand the complex development of youth mental health problems.

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.