title: Examining the Association between Patient-Reported Symptoms of Attention and Memory Dysfunction with Objective Cognitive Performance: A Latent Regression Rasch Model Approach.
abstract: OBJECTIVE
  Patient-reported cognition generally exhibits poor concordance with objectively assessed cognitive performance. In this article, we introduce latent regression Rasch modeling and provide a step-by-step tutorial for applying Rasch methods as an alternative to traditional correlation to better clarify the relationship of self-report and objective cognitive performance. An example analysis using these methods is also included.
  
  
  METHODS
  Introduction to latent regression Rasch modeling is provided together with a tutorial on implementing it using the JAGS programming language for the Bayesian posterior parameter estimates. In an example analysis, data from a longitudinal neurocognitive outcomes study of 132 breast cancer patients and 45 non-cancer matched controls that included self-report and objective performance measures pre- and post-treatment were analyzed using both conventional and latent regression Rasch model approaches.
  
  
  RESULTS
  Consistent with previous research, conventional analysis and correlations between neurocognitive decline and self-reported problems were generally near zero. In contrast, application of latent regression Rasch modeling found statistically reliable associations between objective attention and processing speed measures with self-reported Attention and Memory scores.
  
  
  CONCLUSIONS
  Latent regression Rasch modeling, together with correlation of specific self-reported cognitive domains with neurocognitive measures, helps to clarify the relationship of self-report with objective performance. While the majority of patients attribute their cognitive difficulties to memory decline, the Rash modeling suggests the importance of processing speed and initial learning. To encourage the use of this method, a step-by-step guide and programming language for implementation is provided. Implications of this method in cognitive outcomes research are discussed.
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA liy12@mskcc.org.
authors.forename: Yuelin
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
authors.forename: James C
authors.initials: JC
authors.lastname: Root
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Atkinson
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
authors.forename: Tim A
authors.initials: TA
authors.lastname: Ahles
mesh_terms: D001289:Attention Deficit Disorder with Hyperactivity / Q000150:complications* / Q000523:psychology*; D001499:Bayes Theorem; D003072:Cognition Disorders / Q000175:diagnosis / Q000209:etiology*; D005260:Female; D006801:Humans; D008297:Male; D008569:Memory Disorders / Q000150:complications* / Q000523:psychology*; D009483:Neuropsychological Tests; D011788:Quality of Life; D012044:Regression Analysis; D057566:Self Report; D014706:Verbal Learning / Q000502:physiology
keywords: Learning and memory; Quality of life; Statistical methods