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Using the PDD Behavior Inventory as a Level 2 Screener: A Classification and Regression Trees Analysis

Abstract

In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without ASD. Discrimination accuracy exceeded 80 %, generalized to an independent validation set, and generalized across age groups and sites, and agreed well with ADOS classifications. Parent PDDBIs yielded better results than teacher PDDBIs but, when CART predictions agreed across informants, sensitivity increased. Results also revealed three subtypes of ASD: minimally verbal, verbal, and atypical; and two, relatively common subtypes of non-ASD children: social pragmatic problems and good social skills. These subgroups corresponded to differences in behavior profiles and associated bio-medical findings.

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Acknowledgments

The authors would like to thank the many families that participated in these studies, Deborah Fein for her advice on sensitivity and specificity levels, and Diana Robins for her recommendations on use of LR+ and LR−. This research was supported by funds from the New York State Office for People with Developmental Disabilities, the NYS Special Legislative Grant for Autism Research, by Grant #12-FY99-211 from the March of Dimes Birth Defects Foundation to Ira L. Cohen, by Ongwanada Research Fund to Xudong Liu, and by Grant #PO1-HD047281 to Judith M. Gardner. The PDDBI generates a royalty, 50 % of which is used to support research at the Institute with the other 50 % distributed to the authors of the PDDBI.

Author Contributions

Dr. Cohen prepared the manuscript, taking comments of the co-authors into account, did the statistical analyses, assisted in diagnosing cases and sustained collaboration with the other authors. Drs. Liu and Hudson performed the genetic analyses, confirmed the clinical and ADI-R diagnoses, and supplied their PDDBI data. Drs. Romanczyk, Gillis and Cavalari performed the diagnostic work-ups on their sample and supplied the PDDBI data. Drs. Karmel and Gardner provided the clinical, ADOS and PDDBI data on the children participating in their studies. All authors reviewed the manuscript.

Funding

This research was supported in part by funds from the New York State Office for People with Developmental Disabilities, the NYS Special Legislative Grant for Autism Research, by Grant #12-FY99-211 from the March of Dimes Birth Defects Foundation to Ira L. Cohen, by Ongwanada Research Fund to Xudong Liu, and by Grant #PO1-HD047281 to Judith M. Gardner.

Author information

Correspondence to Ira L. Cohen.

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Conflict of interest

The PDDBI generates a royalty, 50 % of which is used to support research at the Institute with the other 50 % distributed to the authors of the PDDBI and Dr. Cohen is one of the authors. Other authors declare no conflicts of interest.

Ethical Approval

All procedures performed in the research studies described above were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all caregivers participating in the various research projects included in this study.

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Cohen, I.L., Liu, X., Hudson, M. et al. Using the PDD Behavior Inventory as a Level 2 Screener: A Classification and Regression Trees Analysis. J Autism Dev Disord 46, 3006–3022 (2016). https://doi.org/10.1007/s10803-016-2843-0

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Keywords

  • Level 2 screeners
  • Autism Spectrum Disorder
  • Decision trees
  • Data mining
  • Machine learning
  • Seizures
  • Monoamine Oxidase A
  • Genotype
  • Phenotype
  • Subgroups