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Naslov:Challenge of missing data in observational studies : investigating cross-sectional imputation methods for assessing disease activity in axial spondyloarthritis
Avtorji:ID Georgiadis, Stylianos (Avtor)
ID Pons, Marion (Avtor)
ID Rasmussen, Simon Horskjær (Avtor)
ID Lund Hetland, Merete (Avtor)
ID Linde, Louise (Avtor)
ID Di Giuseppe, Daniela (Avtor)
ID Michelsen, Brigitte (Avtor)
ID Wallman, Johan Karlsson (Avtor)
ID Olofsson, Tor (Avtor)
ID Závada, Jakub (Avtor)
ID Rotar, Žiga (Avtor)
ID Perdan-Pirkmajer, Katja (Avtor), et al.
Datoteke:.pdf PDF - Predstavitvena datoteka, prenos (1,38 MB)
MD5: 55237AA3BBCBA2BC873576774D7F5200
 
URL URL - Izvorni URL, za dostop obiščite https://rmdopen.bmj.com/content/11/1/e004844
 
Jezik:Angleški jezik
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:Logo UKC LJ - Univerzitetni klinični center Ljubljana
Povzetek: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.
Ključne besede:axial spondyloarthritis, epidemiology, interleukin-17, tumour necrosis factor inhibitors
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Leto izida:2025
Št. strani:str. 1-14
Številčenje:Vol. 11, iss. 1, [article no.] e004844
PID:20.500.12556/DiRROS-27869 Novo okno
UDK:616-002
ISSN pri članku:2056-5933
DOI:10.1136/rmdopen-2024-004844 Novo okno
COBISS.SI-ID:228786947 Novo okno
Opomba:Nasl. z nasl. zaslona; Opis vira z dne 12. 3. 2025;
Datum objave v DiRROS:26.02.2026
Število ogledov:229
Število prenosov:113
Metapodatki:XML DC-XML DC-RDF
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Gradivo je del revije

Naslov:RMD open
Založnik:BMJ
ISSN:2056-5933
COBISS.SI-ID:32418009 Novo okno

Licence

Licenca:CC BY-NC 4.0, Creative Commons Priznanje avtorstva-Nekomercialno 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by-nc/4.0/deed.sl
Opis:Licenca Creative Commons, ki prepoveduje komercialno uporabo, vendar uporabniki ne rabijo upravljati materialnih avtorskih pravic na izpeljanih delih z enako licenco.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:aksialni spondiloartritis, epidemiologija, interlevkin-17, zaviralci faktorja tumorske nekroze


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