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Impact of missing data mechanism on the estimate of change: a case study on cognitive function and polypharmacy among older persons

Authors Lavikainen P, Leskinen E, Hartikainen S, Möttönen J, Sulkava R, Korhonen MJ

Received 19 August 2014

Accepted for publication 24 October 2014

Published 4 February 2015 Volume 2015:7 Pages 169—180

DOI https://doi.org/10.2147/CLEP.S72918

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 5

Editor who approved publication: Professor Henrik Toft Sørensen


Piia Lavikainen,1,2 Esko Leskinen,3 Sirpa Hartikainen,1,2 Jyrki Möttönen,4 Raimo Sulkava,5 Maarit J Korhonen6

1Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; 2School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland; 3Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland; 4Department of Social Research, University of Helsinki, Helsinki, Finland; 5Department of Geriatrics, Institute of Public Health and Clinical Nutrition, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland; 6Department of Pharmacology, Drug Development and Therapeutics, University of Turku, Turku, Finland

Abstract: Longitudinal studies typically suffer from incompleteness of data. Attrition is a major problem in studies of older persons since participants may die during the study or are too frail to participate in follow-up examinations. Attrition is typically related to an individual’s health; therefore, ignoring it may lead to too optimistic inferences, for example, about cognitive decline or changes in polypharmacy. The objective of this study is to compare the estimates of level and slope of change in 1) cognitive function and 2) number of drugs in use between the assumptions of ignorable and non-ignorable missingness. This study demonstrates the usefulness of latent variable modeling framework. The results suggest that when the missing data mechanism is not known, it is preferable to conduct analyses both under ignorable and non-ignorable missing data assumptions.

Keywords: attrition, latent variable modeling, longitudinal, Mini-Mental State Examination, number of drugs, older persons

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