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A longitudinal item response model for Aberrant Behavior Checklist (ABC) data from children with autism

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Abstract

This manuscript aims to present the first item response theory (IRT) model within a pharmacometric framework to characterize the longitudinal changes of Aberrant Behavior Checklist (ABC) data in children with autism. Data were obtained from 120 patients, which included 20,880 observations of the 58 items for up to three months. Observed scores for each ABC item were modeled as a function of the subject's disability. Longitudinal IRT models with five latent disability variables based on ABC subscales were used to describe the irritability, lethargy, stereotypic behavior, hyperactivity, and inappropriate speech over time. The IRT pharmacometric models could accurately describe the longitudinal changes of the patient's disability while estimating different time-course of disability for the subscales. For all subscales, model-estimated disability was reduced following initiation of therapy, most markedly for hyperactivity. The developed framework provides a description of ABC longitudinal data that can be a suitable alternative to traditional ABC data collected in autism clinical trials. IRT is a powerful tool with the ability to capture the heterogeneous nature of ABC, which results in more accurate analysis in comparison to traditional approaches.

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References

  1. Edition F, Association AP (2013) Diagnostic and statistical manual of mental disorders. American Psychiatric Publishing, Arlington

    Google Scholar 

  2. Matson JL, Kozlowski AM (2011) The increasing prevalence of autism spectrum disorders. Res Autism Spectr Disorders 5(1):418–425

    Article  Google Scholar 

  3. Elsabbagh M, Divan G, Koh YJ, Kim YS, Kauchali S, Marcín C, Montiel-Nava C, Patel V, Paula CS, Wang C (2012) Global prevalence of autism and other pervasive developmental disorders. Autism Res 5(3):160–179

    Article  PubMed  PubMed Central  Google Scholar 

  4. Baio J, Wiggins L, Christensen DL, Maenner MJ, Daniels J, Warren Z, Kurzius-Spencer M, Zahorodny W, Rosenberg CR, White T (2018) Prevalence of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014. MMWR Surveill Summ 67(6):1

    Article  PubMed  PubMed Central  Google Scholar 

  5. Autism IDDMNSYP (2014) Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morb Mortal Wkly Rep Surveill Summ 63(2):1–21

    Google Scholar 

  6. Samadi SA, Mahmoodizadeh A, McConkey R (2012) A national study of the prevalence of autism among five-year-old children in Iran. Autism 16(1):5–14

    Article  PubMed  Google Scholar 

  7. Loomes R, Hull L, Mandy WPL (2017) What is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis. J Am Acad Child Adolesc Psychiatry 56(6):466–474

    Article  PubMed  Google Scholar 

  8. Masi A, DeMayo MM, Glozier N, Guastella AJ (2017) An overview of autism spectrum disorder, heterogeneity and treatment options. Neurosci Bull 33(2):183–193

    Article  PubMed  PubMed Central  Google Scholar 

  9. Siegel M, Beaulieu AA (2012) Psychotropic medications in children with autism spectrum disorders: a systematic review and synthesis for evidence-based practice. J Autism Dev Disord 42(8):1592–1605

    Article  PubMed  Google Scholar 

  10. Aman MG, Singh NN, Stewart AW, Field CJ (1985) The aberrant behavior checklist: a behavior rating scale for the assessment of treatment effects. Am J Mental Defic 89:485–491

    CAS  Google Scholar 

  11. Aman M (2012) Aberrant Behavior Checklist: Current identity and future developments. Clin Exp Pharmacol 2(3):2161–1459

    Article  Google Scholar 

  12. Oro AB, Navarro-Calvillo ME, Esmer C (2014) Autistic Behavior Checklist (ABC) and its applications. In: Patel VB, Preedy VR, Martin CR (eds) Comprehensive guide to autism. Springer, New York, pp 2787–2798

    Google Scholar 

  13. Posey DJ, Stigler KA, Erickson CA, McDougle CJ (2008) Antipsychotics in the treatment of autism. J Clin Investig 118(1):6–14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. McDougle CJ, Scahill L, Aman MG, McCracken JT, Tierney E, Davies M, Arnold LE, Posey DJ, Martin A, Ghuman JK (2005) Risperidone for the core symptom domains of autism: results from the study by the autism network of the research units on pediatric psychopharmacology. Am J Psychiatry 162(6):1142–1148

    Article  PubMed  Google Scholar 

  15. Nagaraj R, Singhi P, Malhi P (2006) Risperidone in children with autism: randomized, placebo-controlled, double-blind study. J Child Neurol 21(6):450–455

    Article  PubMed  Google Scholar 

  16. Pandina GJ, Bossie CA, Youssef E, Zhu Y, Dunbar F (2007) Risperidone improves behavioral symptoms in children with autism in a randomized, double-blind, placebo-controlled trial. J Autism Dev Disord 37(2):367–373

    Article  PubMed  Google Scholar 

  17. Shea S, Turgay A, Carroll A, Schulz M, Orlik H, Smith I, Dunbar F (2004) Risperidone in the treatment of disruptive behavioral symptoms in children with autistic and other pervasive developmental disorders. Pediatrics 114(5):e634–e641

    Article  PubMed  Google Scholar 

  18. Baker FB (2001) The basics of item response theory. ERIC, Washington

    Google Scholar 

  19. Doostfatemeh M, Ayatollah SMT, Jafari P (2016) Power and sample size calculations in clinical trials with patient-reported outcomes under equal and unequal group sizes based on graded response model: a simulation study. Value Health 19(5):639–647

    Article  PubMed  Google Scholar 

  20. Chang C-H, Reeve BB (2005) Item response theory and its applications to patient-reported outcomes measurement. Eval Health Prof 28(3):264–282

    Article  PubMed  Google Scholar 

  21. Cappelleri JC, Lundy JJ, Hays RD (2014) Overview of classical test theory and item response theory for the quantitative assessment of items in developing patient-reported outcomes measures. Clin Ther 36(5):648–662

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ard MC, Galasko DR, Edland SD (2013) Improved statistical power of Alzheimer clinical trials by item-response theory: proof of concept by application to the activities of daily living scale. Alzheimer Dis Assoc Disord 27(2):187

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ueckert S, Plan EL, Ito K, Karlsson MO, Corrigan B, Hooker AC, AsDN I (2014) Improved utilization of ADAS-cog assessment data through item response theory based pharmacometric modeling. Pharm Res 31(8):2152–2165

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Balsis S, Unger AA, Benge JF, Geraci L, Doody RS (2012) Gaining precision on the Alzheimer’s Disease Assessment Scale-cognitive: a comparison of item response theory-based scores and total scores. Alzheimer's Dementia 8(4):288–294

    Article  PubMed  Google Scholar 

  25. Vandemeulebroecke M, Bornkamp B, Krahnke T, Mielke J, Monsch A, Quarg P (2017) A longitudinal item response theory model to characterize cognition over time in elderly subjects. CPT 6(9):635–641

    CAS  Google Scholar 

  26. Novakovic AM, Krekels EH, Munafo A, Ueckert S, Karlsson MO (2017) Application of item response theory to modeling of expanded disability status scale in multiple sclerosis. AAPS J 19(1):172–179

    Article  CAS  PubMed  Google Scholar 

  27. Krekels E, Novakovic A, Vermeulen A, Friberg L, Karlsson M (2017) Item response theory to quantify longitudinal placebo and paliperidone effects on PANSS scores in schizophrenia. CPT 6(8):543–551

    CAS  Google Scholar 

  28. Gottipati G, Karlsson MO, Plan EL (2017) Modeling a composite score in Parkinson’s disease using item response theory. AAPS J 19(3):837–845

    Article  PubMed  Google Scholar 

  29. Buatois S, Retout S, Frey N, Ueckert S (2017) Item response theory as an efficient tool to describe a heterogeneous clinical rating scale in de novo idiopathic Parkinson’s disease patients. Pharm Res 34(10):2109–2118

    Article  CAS  PubMed  Google Scholar 

  30. Association WM (2009) Declaration of Helsinki. Ethical principles for medical research involving human subjects, Association WM, Williamsburg

    Google Scholar 

  31. Hays RD, Morales LS, Reise SP (2000) Item response theory and health outcomes measurement in the 21st century. Med Care 38(9 Suppl):II28

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Nguyen TH, Han H-R, Kim MT, Chan KS (2014) An introduction to item response theory for patient-reported outcome measurement. Pat-Pat-Centered Outcomes Res 7(1):23–35

    Article  Google Scholar 

  33. Doostfatemeh M, Ayatollahi SMT, Jafari P (2015) Testing parent dyad interchangeability in the parent proxy-report of PedsQL™ 4.0: a differential item functioning analysis. Qual Life Res 24(8):1939–1947

    Article  PubMed  Google Scholar 

  34. Murray R, Correll CU, Reynolds GP, Taylor D (2017) Atypical antipsychotics: recent research findings and applications to clinical practice: proceedings of a symposium presented at the 29th Annual European College of Neuropsychopharmacology Congress, 19 September 2016, Vienna, Austria. Therap Adv Psychopharmacol 7(1_suppl):1–14

    Article  Google Scholar 

  35. Guiastrennec B, Sonne DP, Bergstrand M, Vilsbøll T, Knop FK, Karlsson MO (2018) Model-based prediction of plasma concentration and enterohepatic circulation of total bile acids in humans. CPT 7(9):603–612

    CAS  Google Scholar 

  36. Ueckert S (2018) Modeling composite assessment data using item response theory. CPT 7(4):205–218. https://doi.org/10.1002/psp4.12280

    Article  CAS  Google Scholar 

  37. Beal S, Sheiner L, Boeckmann A, Bauer R (2009) NONMEM User’s Guides (1989–2009). Icon Development Solutions, Ellicott City

    Google Scholar 

  38. Karlsson MO, Holford N (2008) A tutorial on visual predictive checks. In: Annual meeting of the population approach group in Europe

  39. Mandell DS, Novak MM, Zubritsky CD (2005) Factors associated with age of diagnosis among children with autism spectrum disorders. Pediatrics 116(6):1480–1486

    Article  PubMed  Google Scholar 

  40. Fountain C, King MD, Bearman PS (2011) Age of diagnosis for autism: individual and community factors across 10 birth cohorts. J Epidemiol Community Health 65(6):503–510

    Article  PubMed  Google Scholar 

  41. Charman T, Baird G (2002) Practitioner review: diagnosis of autism spectrum disorder in 2-and 3-year-old children. J Child Psychol Psychiatry 43(3):289–305

    Article  PubMed  Google Scholar 

  42. Kutcher SP (2005) Risperidone treatment of autistic disorder: longer-term benefits and blinded discontinuation after 6 months. Child Adolesc Psychopharmacol News 10(3):1A

    Article  Google Scholar 

  43. Aman MG, Kasper W, Manos G, Mathew S, Marcus R, Owen R, Mankoski R (2010) Line-item analysis of the Aberrant Behavior Checklist: results from two studies of aripiprazole in the treatment of irritability associated with autistic disorder. J Child Adolesc Psychopharmacol 20(5):415–422

    Article  PubMed  Google Scholar 

  44. Zhang L, Beal SL, Sheiner LB (2003) Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance. J Pharmacokin Pharmacodyn 30(6):387–404

    Article  Google Scholar 

  45. Ackerman TA, Gierl MJ, Walker CM (2003) Using multidimensional item response theory to evaluate educational and psychological tests. Educ Measure 22(3):37–51

    Article  Google Scholar 

  46. Schindler E, Friberg LE, Lum BL, Wang B, Quartino A, Li C, Girish S, Jin JY, Karlsson MO (2018) A pharmacometric analysis of patient-reported outcomes in breast cancer patients through item response theory. Pharm Res 35(6):122

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Ribbing J, Nyberg J, Caster O, Jonsson EN (2007) The lasso—a novel method for predictive covariate model building in nonlinear mixed effects models. J Pharmacokinet Pharmacodyn 34(4):485–517

    Article  PubMed  Google Scholar 

  48. Haem E, Harling K, Ayatollahi SMT, Zare N, Karlsson MO (2017) Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models. J Pharmacokinet Pharmacodyn 44(1):55–66

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by Grant No. 97-01-81-17380 from Shiraz University of Medical Sciences Research Council and the Swedish Research Council Grant 2018-03317.

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Correspondence to Marziyeh Doostfatemeh.

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Haem, E., Doostfatemeh, M., Firouzabadi, N. et al. A longitudinal item response model for Aberrant Behavior Checklist (ABC) data from children with autism. J Pharmacokinet Pharmacodyn 47, 241–253 (2020). https://doi.org/10.1007/s10928-020-09686-0

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