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Quality of Life Research

, Volume 23, Issue 4, pp 1049–1058 | Cite as

Parent-reported cognition of children with cancer and its potential clinical usefulness

  • Jin-Shei Lai
  • Frank Zelko
  • Kevin R. Krull
  • David Cella
  • Cindy Nowinski
  • Peter E. Manley
  • Stewart Goldman
Article

Abstract

Purpose

Cognitive dysfunction is a common concern for children with brain tumors (BTs) or those receiving central nervous system (CNS) toxic cancer treatments. Perceived cognitive function (PCF) is an economical screening that may be used to trigger full, formal cognitive testing. We assessed the potential clinical utility of PCF by comparing parent-reported scores for children with cancer with scores from the general US population.

Methods

Children (n = 515; mean age = 13.5 years; 57.0 % male) and one of their parents were recruited from pediatric oncology clinics. Most children (53.3 %) had a diagnosis of CNS tumor with an average time since diagnosis of 5.6 years. PCF was evaluated using the pediatric PCF item bank (pedsPCF), which was developed and normed on a sample drawn from the US general pediatric population. Children also completed computer-based neuropsychological tests. We tested relationships between PCF and clinical variables. Differential item functioning (DIF) was used to evaluate measurement bias between the samples.

Results

No item showed DIF, supporting the use of pedsPCF in the cancer sample. PedsPCF differentiated children with (vs. without) a BT, p < 0.01, and groups defined by years since diagnosis, p < 0.01. It significantly (p < 0.05) correlated with computerized neuropsychological tests in 40 of 60 comparisons. Children with BTs were rated as having worse pedsPCF scores than the norm, regardless of years since diagnosis.

Conclusions

PCF significantly differentiated cancer survivors with various clinical characteristics. It is brief and easy to implement. PCF should be considered for routine care of pediatric cancer survivors.

Keywords

Perceived cognitive function Item bank Pediatric cancer Brain tumor Item response theory Quality of life 

Notes

Acknowledgments

This study was supported by the National Cancer Institute at the National Institutes of Health (R01CA174452; Principle Investigator: Jin-Shei Lai).

References

  1. 1.
    Howlander, N., Noone, A. M., Krapcho, M., Neyman, N., Aminou, R., Waldron, W., et al. (2011). SEER cancer statistics review, 1975–2008. In N. C. Institute. (Ed.) (Vol. based on November 2010 SEER data submission, posted on the SEER web site). Bethesda, MD.Google Scholar
  2. 2.
    Oeffinger, K. C., Mertens, A. C., Sklar, C. A., Kawashima, T., Hudson, M. M., Meadows, A. T., et al. (2006). Chronic health conditions in adult survivors of childhood cancer. New England Journal of Medicine, 355(15), 1572–1582.PubMedCrossRefGoogle Scholar
  3. 3.
    Waber, D. P., Carpentieri, S. C., Klar, N., Silverman, L. B., Schwenn, M., Hurwitz, C. A., et al. (2000). Cognitive sequelae in children treated for acute lymphoblastic leukemia with dexamethasone or prednisone. Journal of Pediatric Hematology/oncology, 22(3), 206–213.PubMedCrossRefGoogle Scholar
  4. 4.
    Butler, R. W., & Mulhern, R. K. (2005). Neurocognitive interventions for children and adolescents surviving cancer. Journal of Pediatric Psychology, 30(1), 65–78.PubMedCrossRefGoogle Scholar
  5. 5.
    Mulhern, R. K., Merchant, T. E., Gajjar, A., Reddick, W. E., & Kun, L. E. (2004). Late neurocognitive sequelae in survivors of brain tumours in childhood. Lancet Oncology, 5(7), 399–408.PubMedCrossRefGoogle Scholar
  6. 6.
    Ris, M. D., Packer, R., Goldwein, J., Jones-Wallace, D., & Boyett, J. M. (2001). Intellectual outcome after reduced-dose radiation therapy plus adjuvant chemotherapy for medulloblastoma: A Children’s Cancer Group study. Journal of Clinical Oncology, 19(15), 3470–3476.PubMedGoogle Scholar
  7. 7.
    Moore, B. D., & II, I. (2005). Neurocognitive outcomes in survivors of childhood cancer. Journal of Pediatric Psychology, 30(1), 51–63.PubMedCrossRefGoogle Scholar
  8. 8.
    Ellenberg, L., Liu, Q., Yasui, Y., Gioia, G., Packer, R. J., Mertens, A., et al. (2009). Neurocognitive status in long-term survivors of childhood CNS malignancies: A report from the Childhood Cancer Survivor Study. Neuropsychology, 23(6), 705–717.PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Patenaude, A. F., & Kupst, M. J. (2005). Psychosocial functioning in pediatric cancer. Journal of Pediatric Psychology, 30(1), 9–27.PubMedCrossRefGoogle Scholar
  10. 10.
    Zebrack, B. J., & Zeltzer, L. K. (2003). Quality of life issues and cancer survivorship. Current Problems in Cancer, 27(4), 198–211.PubMedCrossRefGoogle Scholar
  11. 11.
    Lavigne, J. V., & Faier-Routman, J. (1992). Psychological adjustment to pediatric physical disorders: A meta-analytic review. Journal of Pediatric Psychology, 17(2), 133–157.PubMedCrossRefGoogle Scholar
  12. 12.
    Ferguson, R. J., McDonald, B. C., Saykin, A. J., & Ahles, T. A. (2007). Brain structure and function differences in monozygotic twins: Possible effects of breast cancer chemotherapy. Journal of Clinical Oncology, 25(25), 3866–3870.PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    Mahone, E. M., Zabel, T. A., Levey, E., Verda, M., & Kinsman, S. (2002). Parent and self-report ratings of executive function in adolescents with myelomeningocele and hydrocephalus. Child Neuropsychology, 8(4), 258–270.PubMedCrossRefGoogle Scholar
  14. 14.
    Lai, J.-S., Butt, Z., Zelko, F., Cella, D., Krull, K., Kieran, M., et al. (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.PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: SAGE Publications, Inc.Google Scholar
  16. 16.
    Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., et al. (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.PubMedCrossRefGoogle Scholar
  17. 17.
    Weiss, D. J., & Kingsbury, G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361–375.CrossRefGoogle Scholar
  18. 18.
    Ichimura, S., Ohira, T., Kobayashi, M., Kano, T., Akiyama, T., Orii, M., et al. (2010). Assessment of cognitive function before and after surgery for posterior cranial fossa lesions using computerized and conventional tests. Neurologia Medico-Chirurgica, 50(6), 441–448.PubMedCrossRefGoogle Scholar
  19. 19.
    Mollica, C. M., Maruff, P., & Vance, A. (2004). Development of a statistical approach to classifying treatment response in individual children with ADHD. Human Psychopharmacology, 19(7), 445–456.PubMedCrossRefGoogle Scholar
  20. 20.
    Williams, J., Thomas, P. R., Maruff, P., Butson, M., & Wilson, P. H. (2006). Motor, visual and egocentric transformations in children with developmental coordination disorder. Child: Care, Health and Development, 32(6), 633–647.Google Scholar
  21. 21.
    Collie, A., Maruff, P., Makdissi, M., McCrory, P., McStephen, M., & Darby, D. (2003). CogSport: reliability and correlation with conventional cognitive tests used in postconcussion medical evaluations. Clinical Journal of Sport Medicine, 13(1), 28–32.PubMedCrossRefGoogle Scholar
  22. 22.
    Cysique, L. A. J., Maruff, P., Darby, D., & Brew, B. J. (2006). The assessment of cognitive function in advanced HIV-1 infection and AIDS dementia complex using a new computerised cognitive test battery. Archives of Clinical Neuropsychology, 21(2), 185–194.PubMedCrossRefGoogle Scholar
  23. 23.
    Lai, J. S., Zelko, F., Butt, Z., Cella, D., Kieran, M., Krull, K., et al. (2011). Perceived cognitive function reported by parents of the United States pediatric population. Child’s Nervous System, 27(2), 285–293.PubMedCrossRefGoogle Scholar
  24. 24.
    Lai, J. S., Butt, Z., Zelko, F., Cella, D., Krull, K. R., Kieran, M. W., et al. (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.PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Muthen, L. K., & Muthen, B. O. (2006). Mplus user’s guide. Los Angeles, CA: Muthen & Muthen.Google Scholar
  26. 26.
    Lai, J. S., Crane, P. K., & Cella, D. (2006). Factor analysis techniques for assessing sufficient unidimensionality of cancer related fatigue. Quality of Life Research, 15(7), 1179–1190.PubMedCrossRefGoogle Scholar
  27. 27.
    Orlando, M., & Thissen, D. (2003). Further examination of the performance of S-X2, an item fit index for dichotomous item response theory models. Applied Psychological Measurement, 27, 289–298.CrossRefGoogle Scholar
  28. 28.
    Samejima, F. (1997). The graded response model. In W. J. van der Linden & R. Hambleton (Eds.), Handbook of modern item response theory (pp. 85–100). New York: Springer.CrossRefGoogle Scholar
  29. 29.
    Lai, J-S, Cella, D., Choi, S., Junghaenel, D. U., Christodoulou, C., Gershon, R., & Stone, A. (2011) How item banks and their application can influence measurement practice in rehabilitation medicine: A PROMIS fatigue item bank example. Archives of Physical Medicine and Rehabilitation, 92(Suppl 1), S20–S27.Google Scholar
  30. 30.
    Lai, J. S., Teresi, J. A., & Gershon, R. (2005). Procedures for the analysis of differential item functioning (DIF) for small sample sizes. Evaluation and the Health Professions, 28, 283–294.PubMedCrossRefGoogle Scholar
  31. 31.
    Teresi, J. A., Ramirez, M., Lai, J. S., & Silver, S. (2008). Occurrences and sources of differential item functioning (DIF) in patient-reported outcome measures: Description of DIF methods, and review of measures of depression, quality of life and general health. Psychology Science Quarterly, 50(4), 538–612.PubMedCentralPubMedGoogle Scholar
  32. 32.
    Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIFdetect and difwithpar. Medical Care, 44(11 Suppl 3), S115–S123.PubMedCrossRefGoogle Scholar
  33. 33.
    Crane, P. K., Gibbons, L. E., Ocepek-Welikson, K., Cook, K., Cella, D., Narasimhalu, K., et al. (2007). A comparison of three sets of criteria for determining the presence of differential item functioning using ordinal logistic regression. Quality of Life Research, 16(Suppl 1), 69–84.PubMedCrossRefGoogle Scholar
  34. 34.
    Choi, S. W., Gibbons, L. E., & Crane, P. K. (2011). lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations. Journal of Statistical Software, 39(8), 1–30.PubMedCentralPubMedGoogle Scholar
  35. 35.
    Stevens, J. (1996). Applied multivariate statistics for the social sciences. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar
  36. 36.
    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), 130–149.CrossRefGoogle Scholar
  37. 37.
    Silver, C. H. (2000). Ecological validity of neuropsychological assessment in childhood traumatic brain injury. The Journal of head trauma rehabilitation, 15(4), 973–988.PubMedCrossRefGoogle Scholar
  38. 38.
    Gioia, G. A., & Isquith, P. K. (2004). Ecological assessment of executive function in traumatic brain injury. Developmental Psychology, 25(1–2), 135–158.Google Scholar
  39. 39.
    Chaytor, N., & Schmitter-Edgecombe, M. (2003). The ecological validity of neuropsychological tests: A review of the literature on everyday cognitive skills. Neuropsychology Review, 13(4), 181–197.PubMedCrossRefGoogle Scholar
  40. 40.
    Spooner, D. M., & Pachana, N. A. (2006). Ecological validity in neuropsychological assessment: A case for greater consideration in research with neurologically intact populations. Archives of Clinical Neuropsychology, 21(4), 327–337.PubMedCrossRefGoogle Scholar
  41. 41.
    Schwartz, B. L., Perfect, T. J., & Perfect, T. (2002). Introduction: Toward an applied metacognition. In T. J. Perfect and B. L. Schwartz (Eds.), Applied metacognition (pp. 1–10). West Nyack, NY: Cambridge University Press.Google Scholar
  42. 42.
    Flavell, J. H. (1979). Metacognitive and cognitive monitoring: A new area of cognitive developmental inquiry. American Psychologist, 34, 906–911.CrossRefGoogle Scholar
  43. 43.
    Schneider, W., Lockl, K., & Perfect, T. (2002). The development of metacognition knowledge in children and adolescents. T. J. Perfect and B. L. Schwartz (Eds.), Applied metacognition (pp. 224–259). West Nyack, NY: Cambridge University Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Jin-Shei Lai
    • 1
  • Frank Zelko
    • 2
  • Kevin R. Krull
    • 3
  • David Cella
    • 4
  • Cindy Nowinski
    • 4
  • Peter E. Manley
    • 5
  • Stewart Goldman
    • 6
  1. 1.Medical Social Sciences and PediatricsFeinberg School of Medicine at Northwestern UniversityChicagoUSA
  2. 2.Pediatric Neuropsychology Service, Department of Child and Adolescent PsychiatryAnn & Robert H. Lurie Children’s Hospital of ChicagoChicagoUSA
  3. 3.Epidemiology and Cancer ControlSt. Jude Children’s Research HospitalMemphisUSA
  4. 4.Medical Social SciencesFeinberg School of Medicine at Northwestern UniversityChicagoUSA
  5. 5.Hematology/OncologyHarvard Medical School, Children’s Hospital Boston and Dana-Farber Cancer InstituteBostonUSA
  6. 6.Hematology/OncologyAnn & Robert H. Lurie Children’s Hospital of ChicagoChicagoUSA

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