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Nonlinear Zâ score modeling for improved detection of cognitive abnormality
dc.contributor.authorKornak, John
dc.contributor.authorFields, Julie
dc.contributor.authorKremers, Walter
dc.contributor.authorFarmer, Sara
dc.contributor.authorHeuer, Hilary W.
dc.contributor.authorForsberg, Leah
dc.contributor.authorBrushaber, Danielle
dc.contributor.authorRindels, Amy
dc.contributor.authorDodge, Hiroko
dc.contributor.authorWeintraub, Sandra
dc.contributor.authorBesser, Lilah
dc.contributor.authorAppleby, Brian
dc.contributor.authorBordelon, Yvette
dc.contributor.authorBove, Jessica
dc.contributor.authorBrannelly, Patrick
dc.contributor.authorCaso, Christina
dc.contributor.authorCoppola, Giovanni
dc.contributor.authorDever, Reilly
dc.contributor.authorDheel, Christina
dc.contributor.authorDickerson, Bradford
dc.contributor.authorDickinson, Susan
dc.contributor.authorDominguez, Sophia
dc.contributor.authorDomoto‐reilly, Kimiko
dc.contributor.authorFaber, Kelley
dc.contributor.authorFerrall, Jessica
dc.contributor.authorFishman, Ann
dc.contributor.authorFong, Jamie
dc.contributor.authorForoud, Tatiana
dc.contributor.authorGavrilova, Ralitza
dc.contributor.authorGearhart, Deb
dc.contributor.authorGhazanfari, Behnaz
dc.contributor.authorGhoshal, Nupur
dc.contributor.authorGoldman, Jill
dc.contributor.authorGraff‐radford, Jonathan
dc.contributor.authorGraff‐radford, Neill
dc.contributor.authorGrant, Ian M.
dc.contributor.authorGrossman, Murray
dc.contributor.authorHaley, Dana
dc.contributor.authorHsiao, John
dc.contributor.authorHsiung, Robin
dc.contributor.authorHuey, Edward D.
dc.contributor.authorIrwin, David
dc.contributor.authorJones, David
dc.contributor.authorJones, Lynne
dc.contributor.authorKantarci, Kejal
dc.contributor.authorKarydas, Anna
dc.contributor.authorKaufer, Daniel
dc.contributor.authorKerwin, Diana
dc.contributor.authorKnopman, David
dc.contributor.authorKraft, Ruth
dc.contributor.authorKramer, Joel
dc.contributor.authorKukull, Walter
dc.contributor.authorLapid, Maria
dc.contributor.authorLitvan, Irene
dc.contributor.authorLjubenkov, Peter
dc.contributor.authorLucente, Diane
dc.contributor.authorLungu, Codrin
dc.contributor.authorMackenzie, Ian
dc.contributor.authorMaldonado, Miranda
dc.contributor.authorManoochehri, Masood
dc.contributor.authorMcGinnis, Scott
dc.contributor.authorMcKinley, Emily
dc.contributor.authorMendez, Mario
dc.contributor.authorMiller, Bruce
dc.contributor.authorMultani, Namita
dc.contributor.authorOnyike, Chiadi
dc.contributor.authorPadmanabhan, Jaya
dc.contributor.authorPantelyat, Alexander
dc.contributor.authorPearlman, Rodney
dc.contributor.authorPetrucelli, Len
dc.contributor.authorPotter, Madeline
dc.contributor.authorRademakers, Rosa
dc.contributor.authorRamos, Eliana Marisa
dc.contributor.authorRankin, Katherine
dc.contributor.authorRascovsky, Katya
dc.contributor.authorRoberson, Erik D.
dc.contributor.authorRogalski‐miller, Emily
dc.contributor.authorSengdy, Pheth
dc.contributor.authorShaw, Les
dc.contributor.authorStaffaroni, Adam M.
dc.contributor.authorSutherland, Margaret
dc.contributor.authorSyrjanen, Jeremy
dc.contributor.authorTartaglia, Carmela
dc.contributor.authorTatton, Nadine
dc.contributor.authorTaylor, Joanne
dc.contributor.authorToga, Arthur
dc.contributor.authorTrojanowski, John
dc.contributor.authorWang, Ping
dc.contributor.authorWong, Bonnie
dc.contributor.authorWszolek, Zbigniew
dc.contributor.authorBoeve, Brad
dc.contributor.authorBoxer, Adam
dc.contributor.authorRosen, Howard
dc.date.accessioned2020-01-13T15:05:38Z
dc.date.availableWITHHELD_12_MONTHS
dc.date.available2020-01-13T15:05:38Z
dc.date.issued2019-12
dc.identifier.citationKornak, John; Fields, Julie; Kremers, Walter; Farmer, Sara; Heuer, Hilary W.; Forsberg, Leah; Brushaber, Danielle; Rindels, Amy; Dodge, Hiroko; Weintraub, Sandra; Besser, Lilah; Appleby, Brian; Bordelon, Yvette; Bove, Jessica; Brannelly, Patrick; Caso, Christina; Coppola, Giovanni; Dever, Reilly; Dheel, Christina; Dickerson, Bradford; Dickinson, Susan; Dominguez, Sophia; Domoto‐reilly, Kimiko ; Faber, Kelley; Ferrall, Jessica; Fishman, Ann; Fong, Jamie; Foroud, Tatiana; Gavrilova, Ralitza; Gearhart, Deb; Ghazanfari, Behnaz; Ghoshal, Nupur; Goldman, Jill; Graff‐radford, Jonathan ; Graff‐radford, Neill ; Grant, Ian M.; Grossman, Murray; Haley, Dana; Hsiao, John; Hsiung, Robin; Huey, Edward D.; Irwin, David; Jones, David; Jones, Lynne; Kantarci, Kejal; Karydas, Anna; Kaufer, Daniel; Kerwin, Diana; Knopman, David; Kraft, Ruth; Kramer, Joel; Kukull, Walter; Lapid, Maria; Litvan, Irene; Ljubenkov, Peter; Lucente, Diane; Lungu, Codrin; Mackenzie, Ian; Maldonado, Miranda; Manoochehri, Masood; McGinnis, Scott; McKinley, Emily; Mendez, Mario; Miller, Bruce; Multani, Namita; Onyike, Chiadi; Padmanabhan, Jaya; Pantelyat, Alexander; Pearlman, Rodney; Petrucelli, Len; Potter, Madeline; Rademakers, Rosa; Ramos, Eliana Marisa; Rankin, Katherine; Rascovsky, Katya; Roberson, Erik D.; Rogalski‐miller, Emily ; Sengdy, Pheth; Shaw, Les; Staffaroni, Adam M.; Sutherland, Margaret; Syrjanen, Jeremy; Tartaglia, Carmela; Tatton, Nadine; Taylor, Joanne; Toga, Arthur; Trojanowski, John; Wang, Ping; Wong, Bonnie; Wszolek, Zbigniew; Boeve, Brad; Boxer, Adam; Rosen, Howard (2019). "Nonlinear Zâ score modeling for improved detection of cognitive abnormality." Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 11(C): 797-808.
dc.identifier.issn2352-8729
dc.identifier.issn2352-8729
dc.identifier.urihttps://hdl.handle.net/2027.42/152598
dc.description.abstractIntroductionConventional Zâ scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these â adjustedâ Zâ scores better represent whether an individual’s cognitive performance is abnormal. Extreme negative Zâ scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency.MethodsIn this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance).ResultsCorrected Zâ scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjustedâ R2.DiscussionNonlinearly corrected Zâ scores with respect to age, sex, and education with ageâ varying residual standard deviation allow for improved detection of nonâ normative extreme cognitive scores.
dc.publisherWiley Periodicals, Inc.
dc.publisherChapman and Hall/CRC
dc.subject.otherGeneralized additive models
dc.subject.otherShape constrained additive models
dc.subject.otherNonlinear Zâ score correction
dc.subject.otherNeuropsychological testing scores
dc.subject.otherHeterogenous variance modeling
dc.titleNonlinear Zâ score modeling for improved detection of cognitive abnormality
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelNeurology and Neurosciences
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/152598/1/dad2jdadm201908003.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/152598/2/dad2jdadm201908003-sup-0001.pdf
dc.identifier.doi10.1016/j.dadm.2019.08.003
dc.identifier.sourceAlzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
dc.identifier.citedreferenceR. Holtzer, Y. Goldin, M. Zimmerman, M. Katz, H. Buschke, R.B. Lipton. Robust norms for selected neuropsychological tests in older adults. Archives of Clinical Neuropsychology. 23: 2008; 531 â 541
dc.identifier.citedreferenceS. De Santi, E. Pirraglia, W. Barr, J. Babb, S. Williams, K. Rogers, et al. Robust and conventional neuropsychology norms: diagnosis and prediction of ageâ related cognitive decline. Neuropsychology. 22: 2008; 469 â 484
dc.identifier.citedreferenceT.N. Tombaugh, J. Kozak, L. Rees. Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming. Arch Clin Neuropsychol. 14: 1999; 167 â 177
dc.identifier.citedreferenceO. Pedraza, J.A. Lucas, G.E. Smith, R.C. Petersen, N.R. Graffâ Radford, R.J. Ivnik. Robust and expanded norms for the Dementia Rating Scale. Archives of Clinical Neuropsychology. 25: 2010; 347 â 358
dc.identifier.citedreferenceE.M. Tuckerâ Drob, K.E. Johnson, R.N. Jones. The cognitive reserve hypothesis: a longitudinal examination of ageâ associated declines in reasoning and processing speed. Dev Psychol. 45: 2009; 431 â 446
dc.identifier.citedreferenceM. Berres, A.U. Monsch, F. Bernasconi, B. Thalmann, H.B. Stähelin. Normal ranges of neuropsychological tests for the diagnosis of Alzheimer’s disease. Stud Health Technology Inform, 2000; 195 â 202
dc.identifier.citedreferenceS. Weintraub, L. Besser, H.H. Dodge, M. Teylan, S. Ferris, F.C. Goldstein, et al. Version 3 of the Alzheimer disease centers’ neuropsychological test battery in the Uniform Data Set (UDS). Alzheimer Dis Associated Disord. 32: 2018; 10 â 17
dc.identifier.citedreferenceS.M. McCurry, S.D. Edland, L. Teri, W.A. Kukull, J.D. Bowen, W.C. McCormick, et al. The cognitive abilities screening instrument (CASI): data from a cohort of 2524 cognitively intact elderly. Int J Geriatr Psychiatry. 14: 1999; 882 â 888
dc.identifier.citedreferenceT.J. Hastie, R.J. Tibshirani. Generalized Additive Models. 1990; Chapman and Hall/CRC: Boca Raton, Fla
dc.identifier.citedreferenceSimon Wood. Generalized Additive Models: An Introduction with R. 2nd ed.. 2017; Chapman and Hall/CRC: Boca Raton, FL
dc.identifier.citedreferenceN. Pya, S.N. Wood. Shape constrained additive models. Stat Comput. 25: 2015; 543 â 559
dc.identifier.citedreferenceR Core Team. R: A language and environment for statistical computing. 2019; R Foundation for Statistical Computing: Vienna, Austria, Available at: https://www.Râ project.org/. Accessed November 26, 2019.
dc.identifier.citedreferenceR.K. Heaton, S.W. Miller, M.J. Taylor, I. Grant. Revised comprehensive norms for an expanded Halsteadâ Reitan Battery: Demographically adjusted neuropsychological norms for African American and Caucasian adults. 2004; Psychological Assessment Resources: Lutz, FL
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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