Multiple imputation was an efficient method for harmonizing the Mini-mental State Examination with missing item-level data

Richard Burns, Peter Butterworth, Kim Kiely, Allison Bielak, Mary Luszcz, Paul Mitchell, Helen Christensen, Chwee von Sanden, Kaarin Anstey

    Research output: Contribution to journalArticlepeer-review

    24 Citations (Scopus)


    Objective: The Mini-Mental State Examination (MMSE) is used to estimate current cognitive status and as a screen for possible dementia. Missing item-level data are commonly reported. Attention to missing data is particularly important. However, there are concerns that common procedures for dealing with missing data, for example, listwise deletion and mean item substitution, are inadequate. Study Design and Setting: We used multiple imputation (MI) to estimate missing MMSE data in 17,303 participants who were drawn from the Dynamic Analyses to Optimize Aging project, a harmonization project of nine Australian longitudinal studies of aging. Results: Our results indicated differences in mean MMSE scores between those participants with and without missing data, a pattern consistent over age and gender levels. MI inflated MMSE scores, but differences between those imputed and those without missing data still existed. A simulation model supported the efficacy of MI to estimate missing item level, although serious decrements in estimation occurred when 50% or more of item-level data were missing, particularly for the oldest participants. Conclusions: Our adaptation of MI to obtain a probable estimate for missing MMSE item level data provides a suitable method when the proportion of missing item-level data is not excessive.

    Original languageEnglish
    Pages (from-to)787-793
    Number of pages7
    JournalJournal of Clinical Epidemiology
    Issue number7
    Publication statusPublished - Jul 2011

    Bibliographical note

    Funding Information:
    This work was supported by the National Health and Medical Research Council (NHMRC) , grant no. 410215 . The data on which this research is based were drawn from several Australian longitudinal studies including the Australian Longitudinal Study of Aging, Australian Longitudinal Study of Women’s Health, Australian Diabetes, Obesity and Lifestyle Study, Blue Mountain Eye Study, Canberra Longitudinal Study of Aging, Household, Income and Labor Dynamics in Australia study, Melbourne Longitudinal Studies on Healthy Aging, Personality And Total Health Through Life Study, and the Sydney Older Persons Study. These studies were pooled and harmonized for the Dynamic Analyses to Optimise Aging (DYNOPTA) project. DYNOPTA was funded by an NHMRC grant (grant no. 410215 ). All studies would like to thank the participants for volunteering their time to be involved in the respective studies. Details of all studies contributing data to DYNOPTA, including individual study leaders and funding sources, are available on the DYNOPTA Web site ( ). The findings and views reported in this article are those of the authors and not those of the original studies or their respective funding agencies. Kaarin Anstey was funded by an NHMRC fellowship no. 366756 . Peter Butterworth was supported by an NHMRC Career Development Award no. 525410 . Allison Bielak was supported by a Canadian Institutes of Health Research postdoctoral fellowship . The authors thank Prof T. Broe for having read and commented on an earlier version of the manuscript.

    Copyright 2011 Elsevier B.V., All rights reserved.


    • Cognitive impairment
    • Dementia
    • Mini-Mental State Examination
    • Missing data
    • Multiple imputation


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