Childhood psychiatric conditions often require that clinicians make individualized treatment decisions. In an exciting trend in child and adolescent mental health (CAMH) research and practice, there is growing interest and awareness in developing individualized decision rules (IDRs). IDRs use sociodemographic information, illness history, comorbidities, family/interpersonal measures, or biomarkers as inputs and then recommend the type, modality, and intensity or delivery of subsequent treatment(s) as outputs. IDRs provide a framework for personalized medicine: the level of personalization increases in proportion to the amount of useful information (tailoring variables) used as inputs in an IDR. The development of IDRs for the treatment of children with mental illness is an important """"""""next step"""""""" in interventions research. Large scale clinical trials have established efficacious interventions fr many of the common CAMH disorders. However, despite a strong evidence base for treatments that are effective on average, little is known about which children are more or less likely to benefit from one treatment or another. Before IDRs can fulfill their potential, much methodological work remains to be done regarding how to use currently available data to assist in their development. A key step in developing effective IDRs is identifying tailoring variables to use as inputs. Tailoring variables are special types of pre-treatment measures that pinpoint which treatment is best for whom. Existing methods do not explicitly (a) identify which measures are most useful (tailoring variable selection), or (b) combine them in an optimal fashion (tailorin variable feature construction).
The specific aims of this study are:
Aim 1 (Methods). To develop and evaluate a framework for (a) tailoring variable selection and (b) tailoring variable feature construction for individualizing CAMH treatment. The new method will be used to address scientific questions such as: (a) What are the most important measures (e.g., demographic, severity and duration of illness, comorbidity, parental psychopathology, psychosocial environment including family and peer relationships, biological) to use for tailoring treatment? (b) How do we combine them in order to guide treatment decisions (e.g., individual psychotherapy treatment, medication, or their combination)? Aim 2 (Application): To illustrate and evaluate the method by applying it to the Child/Adolescent Anxiety Multimodal Study randomized trial data to construct an IDR for choosing between SSRI, CBT, or SSRI+CBT for children with anxiety disorders. We will apply the new methodology to identify variables and features pinpointing children who are more or less likely to benefit from SSRI, CBT, or combined SSRI+CBT. Results will be used to construct a proposed IDR for pediatric anxiety disorders. We will compare how average symptom and severity outcomes differ on the IDR vs SSRI for all vs CBT for all vs SSRI+CBT for all. This study will provide researchers in CAMH with a novel tool for building IDRs. This study has significant public health impact because it aims to develop tools to personalize treatment for children and adolescents with mental health disorders. This is a high-priority area of research for the National Institute of Mental Health.

Public Health Relevance

This project aims to develop a new tool for deciding how to personalize (that is, individualize or customize) treatment for children and adolescents with mental health disorders. This has important clinical and public-health relevance because, although research has established efficacious treatments for many child and adolescent mental health disorders, little is known about which children are more or less likely to benefit from one treatment or another.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Small Research Grants (R03)
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Study Section
Interventions Committee for Disorders Involving Children and Their Families (ITVC)
Program Officer
Sherrill, Joel
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University of Michigan Ann Arbor
Biostatistics & Other Math Sci
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Ann Arbor
United States
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