BACKGROUND: The Mini-Mental State Examination (MMSE) is widely used in population-based longitudinal studies to quantify cognitive change. However, its poor metrological properties, mainly ceiling/floor effects and varying sensitivity to change, have largely restricted its usefulness. We propose a normalizing transformation that corrects these properties, and makes possible the use of standard statistical methods to analyze change in MMSE scores. METHODS: The normalizing transformation designed to correct at best the metrological properties of MMSE was estimated and validated on two population-based studies (n = 4,889, 20-year follow-up) by cross-validation. The transformation was also validated on two external studies with heterogeneous samples mixing normal and pathological aging, and samples including only demented subjects. RESULTS: The normalizing transformation provided correct inference in contrast with models analyzing the change in crude MMSE that most often lead to biased estimates of risk factors and incorrect conclusions. CONCLUSIONS: Cognitive change can be easily and properly assessed with the normalized MMSE using standard statistical methods such as linear (mixed) models.
BACKGROUND: The Mini-Mental State Examination (MMSE) is widely used in population-based longitudinal studies to quantify cognitive change. However, its poor metrological properties, mainly ceiling/floor effects and varying sensitivity to change, have largely restricted its usefulness. We propose a normalizing transformation that corrects these properties, and makes possible the use of standard statistical methods to analyze change in MMSE scores. METHODS: The normalizing transformation designed to correct at best the metrological properties of MMSE was estimated and validated on two population-based studies (n = 4,889, 20-year follow-up) by cross-validation. The transformation was also validated on two external studies with heterogeneous samples mixing normal and pathological aging, and samples including only demented subjects. RESULTS: The normalizing transformation provided correct inference in contrast with models analyzing the change in crude MMSE that most often lead to biased estimates of risk factors and incorrect conclusions. CONCLUSIONS: Cognitive change can be easily and properly assessed with the normalized MMSE using standard statistical methods such as linear (mixed) models.
Authors: Rubén Rabaneda-Bueno; Norma Torres-Carrillo; José Alberto Ávila-Funes; Luis Miguel Gutiérrez-Robledo; Thalía Gabriela Pérez-Suárez; José Luis Acosta; Sara Torres-Castro; Ana Lilia Fletes-Rayas; Itzae Gutierrez-Hurtado; Elena Sandoval-Pinto; Rosa Cremades; Nora Magdalena Torres-Carrillo Journal: Mol Biol Rep Date: 2021-02-21 Impact factor: 2.316
Authors: May A Beydoun; Jordan Weiss; Hind A Beydoun; Sharmin Hossain; Ana I Maldonado; Botong Shen; Michele K Evans; Alan B Zonderman Journal: Alzheimers Res Ther Date: 2021-06-30 Impact factor: 6.982
Authors: Rafał Badacz; Anna Kabłak-Ziembicka; Małgorzata Urbańczyk-Zawadzka; Robert P Banyś; Piotr Musiałek; Piotr Odrowąż-Pieniążek; Mariusz Trystuła; Jan Ścigalski; Krzysztof Żmudka; Tadeusz Przewłocki Journal: Postepy Kardiol Interwencyjnej Date: 2017-09-25 Impact factor: 1.426