Parent-reported cognition of children with cancer and its potential clinical usefulness
- 455 Downloads
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.
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.
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.
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.
KeywordsPerceived cognitive function Item bank Pediatric cancer Brain tumor Item response theory Quality of life
This study was supported by the National Cancer Institute at the National Institutes of Health (R01CA174452; Principle Investigator: Jin-Shei Lai).
- 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
- 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.Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: SAGE Publications, Inc.Google Scholar
- 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
- 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
- 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.Muthen, L. K., & Muthen, B. O. (2006). Mplus user’s guide. Los Angeles, CA: Muthen & Muthen.Google Scholar
- 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
- 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
- 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
- 35.Stevens, J. (1996). Applied multivariate statistics for the social sciences. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar
- 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
- 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
- 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