title: Application of Latent Variable Methods to the Study of Cognitive Decline When Tests Change over Time.
abstract: BACKGROUND
  The way a construct is measured can differ across cohort study visits, complicating longitudinal comparisons. We demonstrated the use of factor analysis to link differing cognitive test batteries over visits to common metrics representing general cognitive performance, memory, executive functioning, and language.
  
  
  METHODS
  We used data from three visits (over 26 years) of the Atherosclerosis Risk in Communities Neurocognitive Study (N = 14,252). We allowed individual tests to contribute information differentially by race, an important factor to consider in cognitive aging. Using generalized estimating equations, we compared associations of diabetes with cognitive change using general and domain-specific factor scores versus averages of equally weighted standardized test scores.
  
  
  RESULTS
  Factor scores provided stronger associations with diabetes at the expense of greater variability around estimates (e.g., for general cognitive performance, -0.064 standard deviation units/year, standard error = 0.015, vs. -0.041 standard deviation units/year, standard error = 0.014), which is consistent with the notion that factor scores more explicitly address error in measuring assessed traits than averages of standardized tests.
  
  
  CONCLUSIONS
  Factor analysis facilitates use of all available data when measures change over time, and further, it allows objective evaluation and correction for differential item functioning.
authors.affiliation: From the aDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; bJohns Hopkins University Center on Aging and Health, Baltimore, MD; cDepartment of Neurology, Johns Hopkins School of Medicine, Baltimore, MD; dCenter of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS; eDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; fDepartment of Biostatistics, UNC Gillings School of Global Public Health, Chapel Hill, NC; and gDepartment of Medicine, University of Mississippi Medical Center, Jackson, MS.
authors.forename: Alden L
authors.initials: AL
authors.lastname: Gross
authors.forename: Melinda C
authors.initials: MC
authors.lastname: Power
authors.forename: Marilyn S
authors.initials: MS
authors.lastname: Albert
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Deal
authors.forename: Rebecca F
authors.initials: RF
authors.lastname: Gottesman
authors.forename: Michael
authors.initials: M
authors.lastname: Griswold
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Wruck
authors.forename: Thomas H
authors.initials: TH
authors.lastname: Mosley
authors.forename: Josef
authors.initials: J
authors.lastname: Coresh
authors.forename: A Richey
authors.initials: AR
authors.lastname: Sharrett
authors.forename: Karen
authors.initials: K
authors.lastname: Bandeen-Roche
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003071:Cognition; D003072:Cognition Disorders / Q000453:epidemiology* / Q000523:psychology; D015331:Cohort Studies; D003920:Diabetes Mellitus / Q000453:epidemiology*; D056344:Executive Function; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D007802:Language; D008137:Longitudinal Studies; D008297:Male; D008568:Memory; D008875:Middle Aged; D009483:Neuropsychological Tests; D012044:Regression Analysis