A Big Data Approach Toward the Development of New Quantitative Autism Severity Scores from Existing Instruments - Abstract
Autism spectrum disorder (ASD) symptom scores from existing measures are influenced by a range of factors
including age, sex, cognitive ability, language level, co-occurring psychiatric symptoms and behavioral
problems. Inadequate adjustment of assessment scores can lead to reduced specificity and mis-estimation of
autism severity. Adjustment for these factors could enhance research and clinical practice, including more
accurate estimation of true relationships with other variables, including etiologic factors, and better case
identification, particularly for complex cases. In addition, the majority of currently available ASD measures have
reduced ability to track change and response to interventions and are not linked to adaptive functioning, which
is being increasingly recognized as one of the most relevant to long term outcomes in ASD. Considering these
noted limitations, enhancing the performance of current measures could advance clinical practice and future
research. The proposed project will focus on the Social Responsiveness Scale (SRS-2), the most widely used
quantitative symptom questionnaire, and the Autism Diagnostic Observation Schedule–Second Edition (ADOS-
2), the gold-standard clinician observation measure. The investigation will utilize existing multiple high-quality
large data sets and apply modern psychometric approaches in order to create new continuous-range,
regression-adjusted, normative SRS-2 and ADOS-2 scores that can supplement existing scores. These scores
will have better specificity for autism symptom domains, more direct links to adaptive function and, when used
alone or in conjunction with existing scores, may yield greater validity for ASD diagnosis in complex, highly
comorbid cases (Specific Aims 1 and 2). By virtue of having dynamic range and being more specifically related
to core ASD symptom domains, newly created SRS-2 and ADOS-2 scores could have greater utility in
longitudinal applications, including treatment studies and clinical monitoring of intervention response (Specific
Aim 3). Knowledge gained from this innovative secondary data analysis will then be used to develop and pilot
software-implemented scoring algorithms to account for relevant demographic, cognitive and clinical factors
(Specific Aim 4). If successful, this approach will provide a low-cost enhancement to existing, widely-used
measures that can be rapidly disseminated to clinicians and scientists for improving practice and research. The
proposed AREA project will also include three cohorts of undergraduate students to build a cross-departmental
and cross-institutional mental health data science training experience that can be sustained as a future
undergraduate research track and as an international research collaboration focused on autism spectrum
disorder. Students will be involved in all aspects of the project from database building to dissemination and the
project will build key undergraduate research infrastructure as well as augment training for the next generation
of mental health data scientists.
