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

S. Georgiadis, M. Pons, S. Rasmussen, ML. Hetland, L. Linde, D. di Giuseppe, B. Michelsen, JK. Wallman, T. Olofsson, J. Zavada, B. Glintborg, AG. Loft, C. Codreanu, D. Melim, D. Almeida, SA. Provan, TK. Kvien, V. Rantalaiho, R. Peltomaa, B....

. 2025 ; 11 (1) : . [pub] 20250220

Jazyk angličtina Země Anglie, Velká Británie

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc25009838

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.

Aberdeen Centre for Arthritis and Musculoskeletal Health University of Aberdeen Aberdeen UK

Center for Treatment of Rheumatic and Musculoskeletal Diseases Diakonhjemmet Hospital Oslo Norway

Centre for Rheumatic Diseases Tampere University Hospital Tampere Finland

Centre for Rheumatology Research Landspitali University Hospital Reykjavik Iceland

Clinical Epidemiology Division Department of Medicine Solna Karolinska Institutet Solna Sweden

Copenhagen Center for Arthritis Research Center for Rheumatology and Spine Diseases Center of Head and Orthopaedics Rigshospitalet Glostrup Denmark

DANBIO registry Rigshospitalet Glostrup Denmark

Department of Clinical Medicine Aarhus University Aarhus Denmark

Department of Clinical Medicine University of Copenhagen Kobenhavn Denmark

Department of Clinical Sciences Lund Rheumatology Skåne University Hospital Lund University Lund Sweden

Department of Medicine Kanta Häme Central Hospital Hämeenlinna Finland

Department of Rheumatology 1st Faculty of Medicine Charles University Praha Czech Republic

Department of Rheumatology Aarhus University Hospital Aarhus Denmark

Department of Rheumatology East Tallinn Central Hospital Tallinn Estonia

Department of Rheumatology University Hospital Zurich University of Zurich Zurich Switzerland

Department of Rheumatology University Medical Centre Ljubljana Ljubljana Slovenia

Faculty of Medicine and Health Technology Tampere University Tampere Finland

Faculty of Medicine University of Iceland Reykjavik Iceland

Faculty of Medicine University of Ljubljana Ljubljana Slovenia

Faculty of Medicine University of Oslo Oslo Norway

Institute of Rheumatology Prague Czech Republic

Public Health Section Inland Norway University of Applied Sciences Elverum Norway

Research Unit Sørlandet Hospital Kristiansand Norway

Rheumatology Inflammation Center Helsinki University Central Hospital Helsinki Finland

Rheumatology Unit University of Bari Bari Italy

Citace poskytuje Crossref.org

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