# Individualized approaches to determining likelihood of ASD caseness

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $790,323

## Abstract

PROJECT SUMMARY
Ongoing development and validation of screening and diagnostic tools has been a major focus of research in
autism spectrum disorders (ASD) during the past 30 years. Diagnostic validity of several widely used tools has
been established by showing that, in most cases, children with ASD score above established cut-offs, whereas
children with non-ASD score below cut-offs. However, a growing body of literature indicates that sensitivity and
specificity of ASD symptom measures varies significantly based on the characteristics of the study population.
This means that the ability of a given tool to differentiate children with and without ASD is variable, and is affected
by an individual's demographic (age, sex), developmental (cognitive ability, language level), and/or behavioral
(clinically significant behavior problems) profile.
The long-term goal of the proposed research is to transform screening and diagnostic practices through
increased individualization of measure selection and interpretation. Phenotypic heterogeneity of youth referred
for possible ASD is a well-recognized challenge that prevents one-size-fits-all assessment approaches from
being validly employed. Yet, with ever-growing demands for such services, clinical and research entities
increasingly rely on specified batteries to make decisions about triage and diagnosis. While use of standardized
tools offers many advantages, uniform application or interpretation of specific instruments disregards the vast
individual heterogeneity that is a hallmark of ASD. Thus, the field is in need of updated practices, wherein tools
are selected, combined, and interpreted in the context of an individual child's presentation, with specific reference
to how likely it is that scores on a given test or combination of tests will indicate ASD caseness for that child.
Toward this end, the proposed secondary data analysis will identify which tools, and combinations of tools, work
best for identifying ASD in sub-groups of youth with shared demographic, developmental, and/or behavioral
phenotypes (e.g., in toddler girls with phrase speech vs. verbally fluent adolescent boys with clinically elevated
behavior problems). We will analyze data from several widely-used ASD measures, aggregated from more than
17,500 children between the ages of 18 months and 17 years, 11 months. These youth were either clinically
referred for ASD diagnostic assessment, assessed due to heightened risk for ASD or other developmental
delays, or recruited for ASD-focused research projects. They were assigned a best-estimate diagnosis of ASD
or non-ASD (e.g., intellectual disability, ADHD, language disorder) by expert clinicians or clinical-researchers
following a comprehensive assessment. By considering the interplay between individual characteristics and
instrument scores in the largest sample to date, the proposed study will move the field toward more individualized
approaches for establishing ASD caseness. Findings from th...

## Key facts

- **NIH application ID:** 10656512
- **Project number:** 5R01MH128288-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Somer L. Bishop
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $790,323
- **Award type:** 5
- **Project period:** 2022-07-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10656512

## Citation

> US National Institutes of Health, RePORTER application 10656512, Individualized approaches to determining likelihood of ASD caseness (5R01MH128288-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10656512. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*