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How to deal with missing longitudinal data in cost of illness analysis in Alzheimer’s disease—suggestions from the GERAS observational study

Overview of attention for article published in BMC Medical Research Methodology, July 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

news
1 news outlet
policy
1 policy source

Citations

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14 Dimensions

Readers on

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59 Mendeley
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Title
How to deal with missing longitudinal data in cost of illness analysis in Alzheimer’s disease—suggestions from the GERAS observational study
Published in
BMC Medical Research Methodology, July 2016
DOI 10.1186/s12874-016-0188-1
Pubmed ID
Authors

Mark Belger, Josep Maria Haro, Catherine Reed, Michael Happich, Kristin Kahle-Wrobleski, Josep Maria Argimon, Giuseppe Bruno, Richard Dodel, Roy W Jones, Bruno Vellas, Anders Wimo

Abstract

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). 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. 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). 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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 19%
Student > Master 8 14%
Other 6 10%
Student > Doctoral Student 5 8%
Student > Ph. D. Student 4 7%
Other 9 15%
Unknown 16 27%
Readers by discipline Count As %
Medicine and Dentistry 13 22%
Nursing and Health Professions 10 17%
Psychology 4 7%
Social Sciences 3 5%
Agricultural and Biological Sciences 2 3%
Other 13 22%
Unknown 14 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 September 2021.
All research outputs
#2,952,112
of 22,881,154 outputs
Outputs from BMC Medical Research Methodology
#466
of 2,022 outputs
Outputs of similar age
#56,177
of 363,150 outputs
Outputs of similar age from BMC Medical Research Methodology
#10
of 41 outputs
Altmetric has tracked 22,881,154 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,022 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 363,150 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.