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Challenge of missing data in observational studies : investigating cross-sectional imputation methods for assessing disease activity in axial spondyloarthritis

Georgiadis, Stylianos ; Pons, Marion ; Rasmussen, Simon ; Hetland, Merete Lund ; Linde, Louise ; di Giuseppe, Daniela ; Michelsen, Brigitte ; Wallman, Johan K. LU ; Olofsson, Tor LU and Zavada, Jakub , et al. (2025) In RMD Open 11(1).
Abstract

Objectives We aimed to compare various methods for imputing disease activity in longitudinally collected observational data of patients with axial spondyloarthritis (axSpA). Methods We conducted a simulation study on data from 8583 axSpA patients from ten European registries. Disease activity was assessed by the Axial Spondyloarthritis Disease Activity Score (ASDAS) and the corresponding low disease activity (LDA; ASDAS<2.1) state at baseline, 6 and 12 months. We focused on cross-sectional methods which impute missing values of an individual at a particular time point based on the available information from other individuals at that time point. We applied nine single and five multiple imputation methods, covering mean, regression and... (More)

Objectives We aimed to compare various methods for imputing disease activity in longitudinally collected observational data of patients with axial spondyloarthritis (axSpA). Methods We conducted a simulation study on data from 8583 axSpA patients from ten European registries. Disease activity was assessed by the Axial Spondyloarthritis Disease Activity Score (ASDAS) and the corresponding low disease activity (LDA; ASDAS<2.1) state at baseline, 6 and 12 months. We focused on cross-sectional methods which impute missing values of an individual at a particular time point based on the available information from other individuals at that time point. We applied nine single and five multiple imputation methods, covering mean, regression and hot deck methods. The performance of each imputation method was evaluated via relative bias and coverage of 95% confidence intervals for the mean ASDAS and the derived proportion of patients in LDA. Results Hot deck imputation methods outperformed mean and regression methods, particularly when assessing LDA. Multiple imputation procedures provided better coverage than the corresponding single imputation ones. However, none of the evaluated methods produced unbiased estimates with adequate coverage across all time points, with performance for missing baseline data being worse than for missing follow-up data. Predictive mean and weighted predictive mean hot deck imputation procedures consistently provided results with low bias Conclusions This study contributes to the available methods for imputing disease activity in observational research. Hot deck imputation using predictive mean matching exhibited the highest robustness and is thus our suggested approach.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Axial Spondyloarthritis, Epidemiology, Interleukin-17, Tumour Necrosis Factor Inhibitors
in
RMD Open
volume
11
issue
1
article number
e004844
publisher
BMJ Publishing Group
external identifiers
  • scopus:85218791081
  • pmid:39979039
ISSN
2056-5933
DOI
10.1136/rmdopen-2024-004844
language
English
LU publication?
yes
id
33d12ac6-f215-41b2-931e-0d680b84a3c0
date added to LUP
2025-06-24 11:56:46
date last changed
2025-07-08 14:12:33
@article{33d12ac6-f215-41b2-931e-0d680b84a3c0,
  abstract     = {{<p>Objectives We aimed to compare various methods for imputing disease activity in longitudinally collected observational data of patients with axial spondyloarthritis (axSpA). Methods We conducted a simulation study on data from 8583 axSpA patients from ten European registries. Disease activity was assessed by the Axial Spondyloarthritis Disease Activity Score (ASDAS) and the corresponding low disease activity (LDA; ASDAS&lt;2.1) state at baseline, 6 and 12 months. We focused on cross-sectional methods which impute missing values of an individual at a particular time point based on the available information from other individuals at that time point. We applied nine single and five multiple imputation methods, covering mean, regression and hot deck methods. The performance of each imputation method was evaluated via relative bias and coverage of 95% confidence intervals for the mean ASDAS and the derived proportion of patients in LDA. Results Hot deck imputation methods outperformed mean and regression methods, particularly when assessing LDA. Multiple imputation procedures provided better coverage than the corresponding single imputation ones. However, none of the evaluated methods produced unbiased estimates with adequate coverage across all time points, with performance for missing baseline data being worse than for missing follow-up data. Predictive mean and weighted predictive mean hot deck imputation procedures consistently provided results with low bias Conclusions This study contributes to the available methods for imputing disease activity in observational research. Hot deck imputation using predictive mean matching exhibited the highest robustness and is thus our suggested approach.</p>}},
  author       = {{Georgiadis, Stylianos and Pons, Marion and Rasmussen, Simon and Hetland, Merete Lund and Linde, Louise and di Giuseppe, Daniela and Michelsen, Brigitte and Wallman, Johan K. and Olofsson, Tor and Zavada, Jakub and Glintborg, Bente and Loft, Anne G. and Codreanu, Catalin and Melim, Daniel and Almeida, Diogo and Provan, Sella Aarrestad and Kvien, Tore K. and Rantalaiho, Vappu and Peltomaa, Ritva and Gudbjornsson, Bjorn and Palsson, Olafur and Rotariu, Ovidiu and MacDonald, Ross and Rotar, Ziga and Pirkmajer, Katja Perdan and Lass, Karin and Iannone, Florenzo and Ciurea, Adrian and Østergaard, Mikkel and Ørnbjerg, L. M.}},
  issn         = {{2056-5933}},
  keywords     = {{Axial Spondyloarthritis; Epidemiology; Interleukin-17; Tumour Necrosis Factor Inhibitors}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BMJ Publishing Group}},
  series       = {{RMD Open}},
  title        = {{Challenge of missing data in observational studies : investigating cross-sectional imputation methods for assessing disease activity in axial spondyloarthritis}},
  url          = {{http://dx.doi.org/10.1136/rmdopen-2024-004844}},
  doi          = {{10.1136/rmdopen-2024-004844}},
  volume       = {{11}},
  year         = {{2025}},
}