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Access via FulcrumNonlinear Zâ score modeling for improved detection of cognitive abnormality
Kornak, 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
2019-12
Citation
Kornak, 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.
Abstract
IntroductionConventional 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.Publisher
Wiley Periodicals, Inc. Chapman and Hall/CRC
ISSN
2352-8729 2352-8729
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