Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/111762
Title: How to deal with missing longitudinal data in cost of illness analysis in Alzheimer's disease-suggestions from the GERAS observational study
Author: Belger, Mark
Haro Abad, Josep Maria
Reed, Catherine
Happich, Michael
Kahle-Wrobleski, Kristin
Argimón Pallás, José M.
Bruno, Giuseppe
Dodel, Richard
Jones, Roy W.
Vellas, Bruno
Wimo, Anders
Keywords: Malaltia d'Alzheimer
Malalties neurodegeneratives
Salut pública
Alzheimer's disease
Neurodegenerative Diseases
Public health
Issue Date: 18-Jul-2016
Publisher: BioMed Central
Abstract: BACKGROUND: Missing data are a common problem in prospective studies with a long follow-up, and the volume, pattern and reasons for missing data may be relevant when estimating the cost of illness. We aimed to evaluate the effects of different methods for dealing with missing longitudinal cost data and for costing caregiver time on total societal costs in Alzheimer's disease (AD). METHODS: GERAS is an 18-month observational study of costs associated with AD. Total societal costs included patient health and social care costs, and caregiver health and informal care costs. Missing data were classified as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). Simulation datasets were generated from baseline data with 10-40 % missing total cost data for each missing data mechanism. Datasets were also simulated to reflect the missing cost data pattern at 18 months using MAR and MNAR assumptions. Naïve and multiple imputation (MI) methods were applied to each dataset and results compared with complete GERAS 18-month cost data. Opportunity and replacement cost approaches were used for caregiver time, which was costed with and without supervision included and with time for working caregivers only being costed. RESULTS: Total costs were available for 99.4 % of 1497 patients at baseline. For MCAR datasets, naïve methods performed as well as MI methods. For MAR, MI methods performed better than naïve methods. All imputation approaches were poor for MNAR data. For all approaches, percentage bias increased with missing data volume. For datasets reflecting 18-month patterns, a combination of imputation methods provided more accurate cost estimates (e.g. bias: -1 % vs -6 % for single MI method), although different approaches to costing caregiver time had a greater impact on estimated costs (29-43 % increase over base case estimate). CONCLUSIONS: Methods used to impute missing cost data in AD will impact on accuracy of cost estimates although varying approaches to costing informal caregiver time has the greatest impact on total costs. Tailoring imputation methods to the reason for missing data will further our understanding of the best analytical approach for studies involving cost outcomes.
Note: Reproducció del document publicat a: https://doi.org/10.1186/s12874-016-0188-1
It is part of: BMC Medical Research Methodology, 2016, vol. 16, num. 83
Related resource: https://doi.org/10.1186/s12874-016-0188-1
URI: http://hdl.handle.net/2445/111762
ISSN: 1471-2288
Appears in Collections:Articles publicats en revistes (Medicina)

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