Strengthen the application of mental health interventions in diverse care settings by examining community and intervention delivery approaches and how they may affect intervention outcomes.
Personalization of mental health care means that patients will receive treatment that is optimally matched to their individual needs. The selection of initial interventions and subsequent sequencing of approaches should be guided by evidence and accomplished with a minimum of trial and error. Matching individuals to interventions will not only require consideration of the individual’s clinical presentation (e.g., symptoms and functional impairment) but also a broader consideration of characteristics of the individual (e.g., genetic, environmental, developmental, cultural, experiential, individual preferences).Ultimately, personalized care will also require consideration of the characteristics of the candidate interventions themselves (e.g., mechanisms of action, anticipated efficacy/tolerability/safety profile, complexity, patient burden, costs) and characteristics of providers and settings (e.g., provider training/competency, setting capacity/resources).
NIMH encourages newer, more advanced research designs that can be used to examine prescriptive approaches for matching individuals to optimal care over studies in which random assignment is used to allocate individuals to one or more treatment or control condition. Studies that involve randomization of large samples in pursuit of incremental gains in effect sizes, especially without attention to modifiers of response that have implications for personalizing care, would be not be considered responsive to this objective.
- Develop and test more personalized intervention approaches for improved response rates, more complete and rapid remission, and more efficient clinical practice.
Priority areas include:
- Analyzing existing data to identify predictors/moderators of treatment response (e.g., clinical/socio-demographic information, biomarkers, surrogate markers of early response, multi-factorial sets of predictors that contribute to a biosignature associated with response) that can be used to construct algorithms for initial treatment selection or sequencing.
- Conducting prospective studies to identify moderator variables that can be used to characterize patients more likely to benefit from a specific treatment modality, such as pharmacological intervention, specific psychotherapies, combination treatments, or augmentation strategies (e.g., pharmacogenomics studies to identify predictors/moderators of efficacy or safety).
- Pilot-testing personalized treatment algorithms to examine feasibility, patient satisfaction, and clinician acceptability, followed by effectiveness trials for definitive tests of personalized approaches.
- Testing modular or stepped-care approaches for matching treatment components to patient’s preferences or particular areas of greatest need.
- Developing and testing individualized strategies targeted to specific functional impairments (e.g., specific work, school, or social functioning deficits) or to those most at risk for relapse/recurrence in order to maximize the chances of complete recovery and sustained remission.
- Develop and refine alternative designs and analytic approaches that can be used to test personalized, prescriptive approaches to intervening.
Priority areas include:
- Developing novel strategies for assessing inter-subject variability in intervention response (e.g., timing, efficacy, safety) and methods for accounting for such variability in the design and testing of interventions.
- Using statistical methods that improve signal to noise for both potential beneficial and adverse effects, permit the identification of responders early in treatment, address patient characteristics (e.g., age, sex, race/ethnicity, severity, specific dimensions/symptom complexes affected within heterogeneous disorders, co-morbid conditions), and permit meaningful analysis of variability in outcome trajectories.
- Developing alternative dynamic designs that maximize benefit to participants and arrive at definitive outcomes in an efficient manner (e.g., use of interim analyses to adjust randomization schemes or sample sizes to expedite conclusions regarding efficacy/safety).
- Creating designs that incorporate tailoring variables (e.g., clinical data, biomarkers) into participant assignment algorithms and employ sequential randomization and iterative evaluation of treatment effects to test prescriptive intervention approaches (e.g., MOST/SMART adaptive treatment designs).
- Designing randomization strategies that incorporate patient preference or prior treatment response to enhance participant engagement and outcomes.