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Handling missing Mini-Mental State Examination (MMSE) values: Results from a cross-sectional long-term-care study

BACKGROUND: Missing values are commonly encountered on the Mini Mental State Examination (MMSE), particularly when administered to frail older people. This presents challenges for MMSE scoring in research settings. We sought to describe missingness in MMSEs administered in long-term-care facilities...

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Autores principales: Godin, Judith, Keefe, Janice, Andrew, Melissa K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5376251/
https://www.ncbi.nlm.nih.gov/pubmed/28142036
http://dx.doi.org/10.1016/j.je.2016.05.001
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author Godin, Judith
Keefe, Janice
Andrew, Melissa K.
author_facet Godin, Judith
Keefe, Janice
Andrew, Melissa K.
author_sort Godin, Judith
collection PubMed
description BACKGROUND: Missing values are commonly encountered on the Mini Mental State Examination (MMSE), particularly when administered to frail older people. This presents challenges for MMSE scoring in research settings. We sought to describe missingness in MMSEs administered in long-term-care facilities (LTCF) and to compare and contrast approaches to dealing with missing items. METHODS: As part of the Care and Construction project in Nova Scotia, Canada, LTCF residents completed an MMSE. Different methods of dealing with missing values (e.g., use of raw scores, raw scores/number of items attempted, scale-level multiple imputation [MI], and blended approaches) are compared to item-level MI. RESULTS: The MMSE was administered to 320 residents living in 23 LTCF. The sample was predominately female (73%), and 38% of participants were aged >85 years. At least one item was missing from 122 (38.2%) of the MMSEs. Data were not Missing Completely at Random (MCAR), χ(2) (1110) = 1,351, p < 0.001. Using raw scores for those missing <6 items in combination with scale-level MI resulted in the regression coefficients and standard errors closest to item-level MI. CONCLUSIONS: Patterns of missing items often suggest systematic problems, such as trouble with manual dexterity, literacy, or visual impairment. While these observations may be relatively easy to take into account in clinical settings, non-random missingness presents challenges for research and must be considered in statistical analyses. We present suggestions for dealing with missing MMSE data based on the extent of missingness and the goal of analyses.
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spelling pubmed-53762512017-04-07 Handling missing Mini-Mental State Examination (MMSE) values: Results from a cross-sectional long-term-care study Godin, Judith Keefe, Janice Andrew, Melissa K. J Epidemiol Original Article BACKGROUND: Missing values are commonly encountered on the Mini Mental State Examination (MMSE), particularly when administered to frail older people. This presents challenges for MMSE scoring in research settings. We sought to describe missingness in MMSEs administered in long-term-care facilities (LTCF) and to compare and contrast approaches to dealing with missing items. METHODS: As part of the Care and Construction project in Nova Scotia, Canada, LTCF residents completed an MMSE. Different methods of dealing with missing values (e.g., use of raw scores, raw scores/number of items attempted, scale-level multiple imputation [MI], and blended approaches) are compared to item-level MI. RESULTS: The MMSE was administered to 320 residents living in 23 LTCF. The sample was predominately female (73%), and 38% of participants were aged >85 years. At least one item was missing from 122 (38.2%) of the MMSEs. Data were not Missing Completely at Random (MCAR), χ(2) (1110) = 1,351, p < 0.001. Using raw scores for those missing <6 items in combination with scale-level MI resulted in the regression coefficients and standard errors closest to item-level MI. CONCLUSIONS: Patterns of missing items often suggest systematic problems, such as trouble with manual dexterity, literacy, or visual impairment. While these observations may be relatively easy to take into account in clinical settings, non-random missingness presents challenges for research and must be considered in statistical analyses. We present suggestions for dealing with missing MMSE data based on the extent of missingness and the goal of analyses. Elsevier 2016-12-27 /pmc/articles/PMC5376251/ /pubmed/28142036 http://dx.doi.org/10.1016/j.je.2016.05.001 Text en © 2016 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Godin, Judith
Keefe, Janice
Andrew, Melissa K.
Handling missing Mini-Mental State Examination (MMSE) values: Results from a cross-sectional long-term-care study
title Handling missing Mini-Mental State Examination (MMSE) values: Results from a cross-sectional long-term-care study
title_full Handling missing Mini-Mental State Examination (MMSE) values: Results from a cross-sectional long-term-care study
title_fullStr Handling missing Mini-Mental State Examination (MMSE) values: Results from a cross-sectional long-term-care study
title_full_unstemmed Handling missing Mini-Mental State Examination (MMSE) values: Results from a cross-sectional long-term-care study
title_short Handling missing Mini-Mental State Examination (MMSE) values: Results from a cross-sectional long-term-care study
title_sort handling missing mini-mental state examination (mmse) values: results from a cross-sectional long-term-care study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5376251/
https://www.ncbi.nlm.nih.gov/pubmed/28142036
http://dx.doi.org/10.1016/j.je.2016.05.001
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