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Pediatric Perceived Cognitive Functioning: Psychometric Properties and Normative Data of the Dutch Item Bank and Short Form

Published online by Cambridge University Press:  10 June 2019

Jan Pieter Marchal ,
Marieke de Vries ,
Judith Conijn ,
André B Rietman ,
Hanneke IJsselstijn ,
Dick Tibboel ,
Lotte Haverman ,
Heleen Maurice-Stam ,
Kim J Oostrom  and
Martha A Grootenhuis
Jan Pieter Marchal*
Affiliation:
Pediatric Psychosocial Department, Emma Children’s Hospital, Amsterdam UMC University Medical Centers, 1105AZ Amsterdam, The Netherlands Research Priority Area Yield, University of Amsterdam, 1105AZ Amsterdam, The Netherlands
Marieke de Vries
Affiliation:
Pediatric Psychosocial Department, Emma Children’s Hospital, Amsterdam UMC University Medical Centers, 1105AZ Amsterdam, The Netherlands Research Priority Area Yield, University of Amsterdam, 1105AZ Amsterdam, The Netherlands School of Psychology, University of Nottingham Malaysia Campus, Semenyih 43500, Malaysia
Judith Conijn
Affiliation:
Research Institute of Child Development and Education, University of Amsterdam, 1001NG Amsterdam, The Netherlands
André B Rietman
Affiliation:
Department of Pediatric Surgery and Intensive Care, Erasmus MC-Sophia Children’s Hospital, Rotterdam 3015GD, The Netherlands
Hanneke IJsselstijn
Affiliation:
Department of Pediatric Surgery and Intensive Care, Erasmus MC-Sophia Children’s Hospital, Rotterdam 3015GD, The Netherlands
Dick Tibboel
Affiliation:
Department of Pediatric Surgery and Intensive Care, Erasmus MC-Sophia Children’s Hospital, Rotterdam 3015GD, The Netherlands
Lotte Haverman
Affiliation:
Pediatric Psychosocial Department, Emma Children’s Hospital, Amsterdam UMC University Medical Centers, 1105AZ Amsterdam, The Netherlands
Heleen Maurice-Stam
Affiliation:
Pediatric Psychosocial Department, Emma Children’s Hospital, Amsterdam UMC University Medical Centers, 1105AZ Amsterdam, The Netherlands
Kim J Oostrom
Affiliation:
Pediatric Psychosocial Department, Emma Children’s Hospital, Amsterdam UMC University Medical Centers, 1105AZ Amsterdam, The Netherlands Research Priority Area Yield, University of Amsterdam, 1105AZ Amsterdam, The Netherlands
Martha A Grootenhuis
Affiliation:
Pediatric Psychosocial Department, Emma Children’s Hospital, Amsterdam UMC University Medical Centers, 1105AZ Amsterdam, The Netherlands Research Priority Area Yield, University of Amsterdam, 1105AZ Amsterdam, The Netherlands Princess Máxima Center for Pediatric Oncology, Utrecht 3584CS, The Netherlands
*
*Correspondence and reprint requests to: Jan Pieter Marchal, Meibergdreef 9, Room G8-136, 1105AZ Amsterdam, The Netherlands, (+31)20 566 5674. E-mail: [email protected]
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Abstract

Objective:

With increasing numbers of children growing up with conditions that are associated with acquired brain injury, efficient neuropsychological screening for cognitive deficits is pivotal. Brief self-report measures concerning daily complaints can play an important role in such screening. We translated and adapted the pediatric perceived cognitive functioning (PedsPCF) self- and parent-report item bank to Dutch. This study presents (1) psychometric properties, (2) a new short form, and (3) normative data for the short form.

Methods:

A general population sample of children and parents was recruited. Dimensionality of the PedsPCF was assessed using confirmatory factor analyses and exploratory bifactor analyses. Item response theory (IRT) modeling was used to evaluate model fit of the PedsPCF, to identify differential item functioning (DIF), and to select items for the short form. To select short-form items, we also considered the neuropsychological content of items.

Results:

In 1441 families, a parent and/or child participated (response rate 66% at family level). Assessed psychometric properties were satisfactory and the predominantly unidimensional factor structure of the PedsPCF allowed for IRT modeling using the graded response model. One item showed meaningful DIF. For the short form, 10 items were selected.

Conclusions:

In this first study of the PedsPCF outside the United States, studied psychometric properties of the translated PedsPCF were satisfactory, and allowed for IRT modeling. Based on the IRT analyses and the content of items, we proposed a new 10-item short form. Further research should determine the relation of PedsPCF outcomes with neurocognitive measures and its ability to facilitate neuropsychological screening in clinical practice.

Keywords

Pediatric psychologyNeuropsychological testsCognitive dysfunctionSigns and symptomsSubjective cognitive functioningItem response theory
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 25 , Issue 8 , September 2019 , pp. 845 - 856
Copyright
Copyright © INS. Published by Cambridge University Press, 2019. 

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Footnotes

Jan Pieter Marchal and Marieke de Vries are co-first authors.

References

REFERENCES

Baron-Cohen, S., Scott, F.J., Allison, C., Williams, J., Bolton, P., Matthews, F.E., & Brayne, C. (2009). Prevalence of autism-spectrum conditions: UK school-based population study. British Journal of Psychiatry, 194(6), 500–509.CrossRefGoogle ScholarPubMed
Cai, L. & Hansen, M. (2013). Limited-information goodness-of-fit testing of hierarchical item factor models. British Journal of Mathematical and Statistical Psychology, 66(2), 245–276.CrossRefGoogle ScholarPubMed
Cella, D., Schalet, B.D., Kallen, M.A., Lai, J.-S., Cook, K.F., Ruthson, J., & Choi, S.W. (2016). PROsetta Stone Analysis Report, Vol. 3—Neuro-QoL Pediatric Cognitive Function and Pediatric Perceived Cognitive Function. Retrieved from http://www.prosettastone.org/AnalysisReport.Google Scholar
Chalmers, P., Pritikin, J., Robitzsch, A., Zoltak, M., KwonHyun, K., Falk, C.F., & Meade, A. (2017, July 23). Package ‘mirt’. Retrieved from https://cran.r-project.org/web/packages/mirt/mirt.pdf.Google Scholar
Chen, K., Didsbury, M., van Zwieten, A., Howell, M., Kim, S., Tong, A., Howard, K., Nassar, N., Barton, B., Lah, S., Lorenzo, J., Strippoli, G., Palmer, S., Teixeira-Pinto, A., Mackie, F., McTaggart, S., Walker, A., Kara, T., Craig, J.C., & Wong, G. (2018). Neurocognitive and educational outcomes in children and adolescents with CKD: A systematic review and meta-analysis. Clinical Journal of the American Society of Nephrology, 13(3), 387–397.CrossRefGoogle ScholarPubMed
Choi, S.W., Gibbons, L.E., & Crane, P.K. (2016, March 3). Package “lordif”. Retrieved from https://cran.r-project.org/web/packages/lordif/lordif.pdf.Google Scholar
Clancy, O., Edginton, T., Casarin, A., & Vizcaychipi, M.P. (2015). The psychological and neurocognitive consequences of critical illness. A pragmatic review of current evidence. Journal of the Intensive Care Society, 16(3), 226–233.CrossRefGoogle ScholarPubMed
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. New York, NY: Academy Press.Google Scholar
Corp, I. (2016). IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.Google Scholar
Correia, H. (2013). PROMIS Instrument Development and Validation Scientific Standards Version 2.0: Appendix 14. Retrieved from http://www.healthmeasures.net/images/PROMIS/PROMISStandards_Vers2.0_Final.pdf.Google Scholar
Coutinho, V., Camara-Costa, H., Kemlin, I., Billette de Villemeur, T., Rodriguez, D., & Dellatolas, G. (2017). The discrepancy between performance-based measures and questionnaires when assessing clinical outcomes and quality of life in pediatric patients with neurological disorders. Applied Neuropsychology: Child, 6(4), 255–261.CrossRefGoogle ScholarPubMed
de Ruiter, M.A., van Mourik, R., Schouten-van Meeteren, A.Y., Grootenhuis, M.A., & Oosterlaan, J. (2013). Neurocognitive consequences of a paediatric brain tumour and its treatment: A meta-analysis. Developmental Medicine and Child Neurology, 55(5), 408–417.CrossRefGoogle ScholarPubMed
De Vet, H.C., Terwee, C.B., Mokkink, L.B., & Knol, D.L. (2011). Measurement in Medicine: A Practical Guide. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Edelen, M.O. & Reeve, B.B. (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, 16(Suppl.1), 5–18.CrossRefGoogle ScholarPubMed
Field, A. (2013). Discovering Statistics using IBM SPSS Statistics. London, UK: Sage.Google Scholar
Hardouin, J.B., Conroy, R., & Sebille, V. (2011). Imputation by the mean score should be avoided when validating a Patient Reported Outcomes questionnaire by a Rasch model in presence of informative missing data. BMC Medical Research Methodology, 11, 105.CrossRefGoogle ScholarPubMed
Hardy, K.K., Olson, K., Cox, S.M., Kennedy, T., & Walsh, K.S. (2017). Systematic review: A prevention-based model of neuropsychological assessment for children with medical illness. Journal of Pediatric Psychology, 42(8), 815–822.CrossRefGoogle ScholarPubMed
Hijmans, C.T., Fijnvandraat, K., Grootenhuis, M.A., van Geloven, N., Heijboer, H., Peters, M., & Oosterlaan, J. (2011). Neurocognitive deficits in children with sickle cell disease: A comprehensive profile. Pediatric Blood & Cancer, 56(5), 783–788.CrossRefGoogle ScholarPubMed
Hu, L.T. & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.CrossRefGoogle Scholar
Jennrich, R.I. & Bentler, P.M. (2011). Exploratory bi-factor analysis. Psychometrika, 76(4), 537–549.CrossRefGoogle ScholarPubMed
Kang, T. & Chen, T.T. (2010). Performance of the generalized S-X2 item fit index for the graded response model. Asia Pacific Education Review, 12(1), 89–96.CrossRefGoogle Scholar
Koo, T.K. & Li, M.Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155–163.CrossRefGoogle ScholarPubMed
Lai, J.S., Bregman, C., Zelko, F., Nowinski, C., Cella, D., Beaumont, J.J., & Goldman, S. (2017). Parent-reported cognitive function is associated with leukoencephalopathy in children with brain tumors. Quality of Life Research, 26(9), 2541–2550.CrossRefGoogle ScholarPubMed
Lai, J.S., Butt, Z., Zelko, F., Cella, D., Krull, K.R., Kieran, M.W., & Goldman, S. (2011). Development of a parent-report cognitive function item bank using item response theory and exploration of its clinical utility in computerized adaptive testing. Journal of Pediatric Psychology, 36(7), 766–779.CrossRefGoogle ScholarPubMed
Lai, J.S., Wagner, L.I., Jacobsen, P.B., & Cella, D. (2014). Self-reported cognitive concerns and abilities: Two sides of one coin? Psycho-Oncology, 23(10), 1133–1141.CrossRefGoogle ScholarPubMed
Lai, J.S., Zelko, F., Butt, Z., Cella, D., Kieran, M.W., Krull, K.R., Magasi, S., & Goldman, S. (2011). Parent-perceived child cognitive function: Results from a sample drawn from the US general population. Child’s Nervous System, 27(2), 285–293.CrossRefGoogle ScholarPubMed
Lai, J.S., Zelko, F., Krull, K.R., Cella, D., Nowinski, C., Manley, P.E., & Goldman, S. (2014). Parent-reported cognition of children with cancer and its potential clinical usefulness. Quality of Life Research, 23(4), 1049–1058.CrossRefGoogle ScholarPubMed
MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130.CrossRefGoogle Scholar
Maydeu-Olivares, A. & Joe, H. (2014). Assessing approximate fit in categorical data analysis. Multivariate Behavioral Research, 49(4), 305–328.CrossRefGoogle ScholarPubMed
Mehta, S.K. & Richards, N. (2002). Parental involvement in pediatric cardiology outpatient visits. Clinical Pediatrics, 41(8), 593–596.CrossRefGoogle ScholarPubMed
Muthén, L.K. & Muthén, B.O. (1998-2015). Mplus User's Guide (7th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
Passolunghi, M.C., Rueda Ferreira, T.I., & Tomasetto, C. (2014). Math–gender stereotypes and math-related beliefs in childhood and early adolescence. Learning and Individual Differences, 34(Suppl.C), 70–76.CrossRefGoogle Scholar
Polanczyk, G., de Lima, M.S., Horta, B.L., Biederman, J., & Rohde, L.A. (2007). The worldwide prevalence of ADHD: A systematic review and metaregression analysis. American Journal of Psychiatry, 164(6), 942–948.CrossRefGoogle ScholarPubMed
Reeve, B.B., Hays, R.D., Bjorner, J.B., Cook, K.F., Crane, P.K., Teresi, J.A., Thissen, D., Revicki, D.A., Weiss, D.J., Hambleton, R.K., Liu, H., Gershon, R., Reise, S.P., Lai, J.S., Cella, D., & PROMIS Cooperative Group (2007). Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Medical Care, 45(5 Suppl.1), S22–S31.CrossRefGoogle Scholar
Rizopoulos, D. (2013). Package ‘ltm’. Retrieved from https://cran.r-project.org/web/packages/ltm/ltm.pdf.Google Scholar
Samejima, F. (1997). Graded response model, In Handbook of modern item response theory (pp. 85–100). New York (NY): Springer.CrossRefGoogle Scholar
Team, R.C. (2016). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.Google Scholar
Terwee, C.B., Roorda, L.D., de Vet, H.C., Dekker, J., Westhovens, R., van Leeuwen, J., Cella, D., Correia, H., Arnold, B., Perez, B., & Boers, M. (2014). Dutch-Flemish translation of 17 item banks from the patient-reported outcomes measurement information system (PROMIS). Quality of Life Research, 23(6), 1733–1741.Google Scholar
Van den Ark, L.A. (2017). Package ‘Mokken’ Version 2.8.6. Retrieved from https://cran.r-project.org/web/packages/mokken/mokken.pdf.Google Scholar
Vega-Fernandez, P., Zelko, F.A., Klein-Gitelman, M., Lee, J., Hummel, J., Nelson, S., Thomas, E.C., Ying, J., Beebe, D.W., & Brunner, H.I. (2014). Value of questionnaire-based screening as a proxy for neurocognitive testing in childhood-onset systemic lupus erythematosus. Arthritis Care & Research, 66(6), 943–948.CrossRefGoogle ScholarPubMed
Wakefield, C.E., McLoone, J.K., Fleming, C.A.K., Peate, M., Thomas, E.J., Sansom-Daly, U., Butow, P., & Cohn, R.J. (2011). Adolescent cancer and health-related decision-making: An Australian multi-perspective family analysis of appointment attendance and involvement in medical and lifestyle choices. Journal of Adolescent and Young Adult Oncology, 1(4), 173–180.CrossRefGoogle Scholar
Wong, A.W., Lai, J.S., Correia, H., & Cella, D. (2015). Evaluating psychometric properties of the Spanish-version of the pediatric functional assessment of chronic illness therapy-perceived cognitive function (pedsFACIT-PCF). Quality of Life Research, 24(9), 2289–2295.CrossRefGoogle Scholar
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