Handling missing Mini-Mental State Examination (MMSE) values: Results from a cross-sectional long-term-care study
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Judith Godin1, Janice Keefe2, Melissa K Andrew3
1Geriatric Medicine Research Unit, Nova Scotia Health Authority and Dalhousie University, Canada; Department of Family Studies and Gerontology and the Nova Scotia Centre on Aging, Mount Saint Vincent University, Canada.
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), χ2 (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.
Insights
Missing Mini Mental State Examination (MMSE) items are common in frail older adults. A combined approach using raw scores for minor missingness and scale-level multiple imputation (MI) closely matched item-level MI for research accuracy.
Area of Science:
- Gerontology
- Neuropsychological Assessment
- Biostatistics
Background:
- Missing values are frequent in Mini Mental State Examinations (MMSE), especially in frail older populations.
- This poses significant challenges for accurate MMSE scoring in research contexts.
- Understanding missing data patterns is crucial for reliable cognitive assessments.
Purpose of the Study:
- To characterize missingness patterns in MMSE data from long-term-care facility (LTCF) residents.
- To compare various statistical methods for handling missing MMSE items.
- To identify optimal approaches for MMSE data analysis in research.
Main Methods:
- Administered MMSE to 320 LTCF residents in Nova Scotia.
- Compared item-level multiple imputation (MI) with other methods: raw scores, raw scores/items attempted, and scale-level MI.
- Analyzed data for missingness patterns and their impact on statistical outcomes.
Main Results:
- 38.2% of MMSEs had at least one missing item.
- Missingness was not completely random (MCAR), indicating systematic issues.
- A blended approach (raw scores for <6 missing items + scale-level MI) yielded results closest to item-level MI.
Conclusions:
- Missing MMSE items often reflect underlying issues like dexterity, literacy, or vision problems.
- Non-random missingness requires careful consideration in statistical research analyses.
- Proposed strategies for managing missing MMSE data based on extent and analytical goals.
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