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Updated: Jan 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Item-level psychometrics of a brief self-reported memory problem screening measure in breast cancer survivors
Pascal Jean-Pierre1, Ying Cheng2, Raheem J Paxton3
1Department of Behavioral Sciences and Social Medicine, Cancer Neurocognitive Translational Research Laboratory, Florida State University College of Medicine, Tallahassee, FL, USA.
Abstract:
Background: We previously developed and psychometrically validated a self-reported memory problem (SRMP) measure using principal component analysis. In the present study, we applied item response theory (IRT) analysis to further examined the construct validity and determine item-level psychometric properties for the SRMP.Methods: The sample included 530 female breast cancer survivors (61% non-Hispanic White, mean age = 57 years) who were recruited from community-based organizations and large health care systems. We examined the construct validity, item-level psychometric properties, and differential item functioning (DIF) of the SRMP using confirmatory factor analysis (CFA), IRT and logistic regression analysis models.Results: The CFA confirmed a one-factor structure for the SRMP (comparative fit index [CFI] = 0.996, root-mean-square-error-approximation [RMSEA] = 0.059). As expected, SRMP scores correlated significantly with pain, mood, and fatigue; but not spiritual health locus of control (SPR). DIF analysis showed no measurement differences based on race/ethnicity and age groups.Conclusion: The CFA and DIF analysis supported the construct validity of the SRMP and its use in an ethnically diverse breast cancer population. These findings provide further evidence of the generalizability for the SRMP, and support its utilization as a psychometrically valid and reliable screening measure of cancer and treatment-related memory difficulties.
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