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National Database for Autism Research (NDAR): Big Data Opportunities for Health Services Research and Health Technology Assessment

Author

Listed:
  • Nalin Payakachat

    (University of Arkansas for Medical Sciences)

  • J. Mick Tilford

    (University of Arkansas for Medical Sciences
    University of Arkansas for Medical Sciences)

  • Wendy J. Ungar

    (The Hospital for Sick Children
    University of Toronto)

Abstract

The National Database for Autism Research (NDAR) is a US National Institutes of Health (NIH)-funded research data repository created by integrating heterogeneous datasets through data sharing agreements between autism researchers and the NIH. To date, NDAR is considered the largest neuroscience and genomic data repository for autism research. In addition to biomedical data, NDAR contains a large collection of clinical and behavioral assessments and health outcomes from novel interventions. Importantly, NDAR has a global unique patient identifier that can be linked to aggregated individual-level data for hypothesis generation and testing, and for replicating research findings. As such, NDAR promotes collaboration and maximizes public investment in the original data collection. As screening and diagnostic technologies as well as interventions for children with autism are expensive, health services research (HSR) and health technology assessment (HTA) are needed to generate more evidence to facilitate implementation when warranted. This article describes NDAR and explains its value to health services researchers and decision scientists interested in autism and other mental health conditions. We provide a description of the scope and structure of NDAR and illustrate how data are likely to grow over time and become available for HSR and HTA.

Suggested Citation

  • Nalin Payakachat & J. Mick Tilford & Wendy J. Ungar, 2016. "National Database for Autism Research (NDAR): Big Data Opportunities for Health Services Research and Health Technology Assessment," PharmacoEconomics, Springer, vol. 34(2), pages 127-138, February.
  • Handle: RePEc:spr:pharme:v:34:y:2016:i:2:d:10.1007_s40273-015-0331-6
    DOI: 10.1007/s40273-015-0331-6
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    Cited by:

    1. Yoshiyuki Tachibana & Celine Miyazaki & Erika Ota & Rintaro Mori & Yeonhee Hwang & Eriko Kobayashi & Akiko Terasaka & Julian Tang & Yoko Kamio, 2017. "A systematic review and meta-analysis of comprehensive interventions for pre-school children with autism spectrum disorder (ASD)," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-28, December.
    2. Yue Sun & Limei Wang & Kun Gao & Shihui Ying & Weili Lin & Kathryn L. Humphreys & Gang Li & Sijie Niu & Mingxia Liu & Li Wang, 2023. "Self-supervised learning with application for infant cerebellum segmentation and analysis," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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