Challenge of missing data in observational studies: investigating cross-sectional imputation methods for assessing disease activity in axial spondyloarthritis
Jazyk angličtina Země Velká Británie, Anglie Médium electronic
Typ dokumentu časopisecké články
PubMed
39979039
PubMed Central
PMC11843021
DOI
10.1136/rmdopen-2024-004844
PII: rmdopen-2024-004844
Knihovny.cz E-zdroje
- Klíčová slova
- Axial Spondyloarthritis, Epidemiology, Interleukin-17, Tumour Necrosis Factor Inhibitors,
- MeSH
- axiální spondyloartritida * diagnóza epidemiologie MeSH
- dospělí MeSH
- interpretace statistických dat MeSH
- lidé MeSH
- longitudinální studie MeSH
- pozorovací studie jako téma * MeSH
- průřezové studie MeSH
- registrace MeSH
- spondylartritida diagnóza MeSH
- stupeň závažnosti nemoci * MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa epidemiologie MeSH
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
DANBIO registry Rigshospitalet Glostrup Denmark
Department of Clinical Medicine Aarhus University Aarhus Denmark
Department of Clinical Medicine University of Copenhagen Kobenhavn Denmark
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
Zobrazit více v PubMed
Sieper J, Poddubnyy D. Axial spondyloarthritis. The Lancet. 2017;390:73–84. doi: 10.1016/S0140-6736(16)31591-4. PubMed DOI
Sieper J, Rudwaleit M, Baraliakos X, et al. The Assessment of SpondyloArthritis international Society (ASAS) handbook: a guide to assess spondyloarthritis. Ann Rheum Dis. 2009;68 Suppl 2:ii1–44. doi: 10.1136/ard.2008.104018. PubMed DOI
Landewé R, van Tubergen A. Clinical Tools to Assess and Monitor Spondyloarthritis. Curr Rheumatol Rep. 2015;17:47. doi: 10.1007/s11926-015-0522-3. PubMed DOI PMC
Navarro-Compán V, Sepriano A, El-Zorkany B, et al. Axial spondyloarthritis. Ann Rheum Dis. 2021;80:1511–21. doi: 10.1136/annrheumdis-2021-221035. PubMed DOI
Lukas C, Landewé R, Sieper J, et al. Development of an ASAS-endorsed disease activity score (ASDAS) in patients with ankylosing spondylitis. Ann Rheum Dis. 2009;68:18–24. doi: 10.1136/ard.2008.094870. PubMed DOI
Ramiro S, Nikiphorou E, Sepriano A, et al. ASAS-EULAR recommendations for the management of axial spondyloarthritis: 2022 update. Ann Rheum Dis. 2023;82:19–34. doi: 10.1136/ard-2022-223296. PubMed DOI
van der Heijde D, Molto A, Ramiro S, et al. Goodbye to the term ‘ankylosing spondylitis’, hello ‘axial spondyloarthritis’: time to embrace the ASAS-defined nomenclature. Ann Rheum Dis. 2024;83:547–9. doi: 10.1136/ard-2023-225185. PubMed DOI
Machado P, Landewé R, Lie E, et al. Ankylosing Spondylitis Disease Activity Score (ASDAS): defining cut-off values for disease activity states and improvement scores. Ann Rheum Dis. 2011;70:47–53. doi: 10.1136/ard.2010.138594. PubMed DOI
Machado PM, Landewé R, Heijde D van der, et al. Ankylosing Spondylitis Disease Activity Score (ASDAS): 2018 update of the nomenclature for disease activity states. Ann Rheum Dis. 2018;77:1539–40. doi: 10.1136/annrheumdis-2018-213184. PubMed DOI
Stürmer T, Wang T, Golightly YM, et al. Methodological considerations when analysing and interpreting real-world data. Rheumatology (Oxford) 2020;59:14–25. doi: 10.1093/rheumatology/kez320. PubMed DOI PMC
Courvoisier DS, Lauper K, Kedra J, et al. EULAR points to consider when analysing and reporting comparative effectiveness research using observational data in rheumatology. Ann Rheum Dis. 2022;81:780–5. doi: 10.1136/annrheumdis-2021-221307. PubMed DOI
Molto A, Tezenas du Montcel S, Wendling D, et al. Disease activity trajectories in early axial spondyloarthritis: results from the DESIR cohort. Ann Rheum Dis. 2017;76:1036–41. doi: 10.1136/annrheumdis-2016-209785. PubMed DOI
Christiansen SN, Ørnbjerg LM, Rasmussen SH, et al. European bio-naïve spondyloarthritis patients initiating TNF inhibitor: time trends in baseline characteristics, treatment retention and response. Rheumatology (Sunnyvale) 2022;61:3799–807. doi: 10.1093/rheumatology/keab945. PubMed DOI
Little RJA, Rubin DB. Statistical Analysis with Missing Data. 3rd ed. Hoboken, NJ: John Wiley & Sons; 2020.
Buuren S. Flexible Imputation of Missing Data. 2nd ed. Boca Raton, FL: Chapman and Hall/CRC Press; 2018.
Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7:147–77. PubMed
Engels JM, Diehr P. Imputation of missing longitudinal data: a comparison of methods. J Clin Epidemiol. 2003;56:968–76. doi: 10.1016/s0895-4356(03)00170-7. PubMed DOI
Pedersen AB, Mikkelsen EM, Cronin-Fenton D, et al. Missing data and multiple imputation in clinical epidemiological research. Clin Epidemiol. 2017;9:157–66. doi: 10.2147/CLEP.S129785. PubMed DOI PMC
Harel O, Mitchell EM, Perkins NJ, et al. Multiple Imputation for Incomplete Data in Epidemiologic Studies. Am J Epidemiol. 2018;187:576–84. doi: 10.1093/aje/kwx349. PubMed DOI PMC
Lauper K, Kedra J, de Wit M, et al. Analysing and reporting of observational data: a systematic review informing the EULAR points to consider when analysing and reporting comparative effectiveness research with observational data in rheumatology. RMD Open. 2021;7:1–9. doi: 10.1136/rmdopen-2021-001818. PubMed DOI PMC
Twisk J, de Vente W. Attrition in longitudinal studies. How to deal with missing data. J Clin Epidemiol. 2002;55:329–37. doi: 10.1016/s0895-4356(01)00476-0. PubMed DOI
Mongin D, Lauper K, Turesson C, et al. Imputing missing data of function and disease activity in rheumatoid arthritis registers: what is the best technique? RMD Open. 2019;5:e000994. doi: 10.1136/rmdopen-2019-000994. PubMed DOI PMC
Pérez A, Dennis RJ, Gil JFA, et al. Use of the mean, hot deck and multiple imputation techniques to predict outcome in intensive care unit patients in Colombia. Stat Med. 2002;21:3885–96. doi: 10.1002/sim.1391. PubMed DOI
Barzi F, Woodward M. Imputations of missing values in practice: results from imputations of serum cholesterol in 28 cohort studies. Am J Epidemiol. 2004;160:34–45. doi: 10.1093/aje/kwh175. PubMed DOI
Linde L, Ørnbjerg LM, Rasmussen SH, et al. Commonalities and differences in set-up and data collection across European spondyloarthritis registries - results from the EuroSpA collaboration. Arthritis Res Ther. 2023;25:205. doi: 10.1186/s13075-023-03184-7. PubMed DOI PMC
Nordholt ES. Imputation: Methods, Simulation Experiments and Practical Examples. Int Statistical Rev . 1998;66:157–80. doi: 10.1111/j.1751-5823.1998.tb00412.x. DOI
Andridge RR, Little RJA. A Review of Hot Deck Imputation for Survey Non-response. Int Stat Rev . 2010;78:40–64. doi: 10.1111/j.1751-5823.2010.00103.x. PubMed DOI PMC
Morris TP, White IR, Royston P. Tuning multiple imputation by predictive mean matching and local residual draws. BMC Med Res Methodol. 2014;14:75. doi: 10.1186/1471-2288-14-75. PubMed DOI PMC
Siddique J, Belin TR. Multiple imputation using an iterative hot-deck with distance-based donor selection. Stat Med. 2008;27:83–102. doi: 10.1002/sim.3001. PubMed DOI
Burton A, Altman DG, Royston P, et al. The design of simulation studies in medical statistics. Stat Med. 2006;25:4279–92. doi: 10.1002/sim.2673. PubMed DOI
Morris TP, White IR, Crowther MJ. Using simulation studies to evaluate statistical methods. Stat Med. 2019;38:2074–102. doi: 10.1002/sim.8086. PubMed DOI PMC
Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55. doi: 10.1093/biomet/70.1.41. DOI
International Monetary Fund [Internet] GDP per capita, current prices. https://www.imf.org/external/datamapper/NGDPDPC@WEO/OEMDC/ADVEC/WEOWORLD Available.
Tillé Y. Sampling and Estimation from Finite Populations. Hoboken, NJ: John Wiley & Sons; 2020.
Donders ART, van der Heijden GJMG, Stijnen T, et al. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59:1087–91. doi: 10.1016/j.jclinepi.2006.01.014. PubMed DOI
Collins LM, Schafer JL, Kam CM. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol Methods. 2001;6:330–51. PubMed
R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2024.
Buuren S, mice G-O. Multivariate imputation by chained equations in R. J Stat Softw. 2011;45:1–67. doi: 10.18637/jss.v045.i03. DOI
White IR, Pham TM, Quartagno M, et al. How to check a simulation study. Int J Epidemiol. 2024;53:1–7. doi: 10.1093/ije/dyad134. PubMed DOI PMC
Sterne JAC, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. doi: 10.1136/bmj.b2393. PubMed DOI PMC
Hayati Rezvan P, Lee KJ, Simpson JA. The rise of multiple imputation: a review of the reporting and implementation of the method in medical research. BMC Med Res Methodol. 2015;15:30.:30. doi: 10.1186/s12874-015-0022-1. PubMed DOI PMC
Austin PC, White IR, Lee DS, et al. Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Can J Cardiol. 2021;37:1322–31. doi: 10.1016/j.cjca.2020.11.010. PubMed DOI PMC
Curnow E, Carpenter JR, Heron JE, et al. Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified. J Clin Epidemiol. 2023;160:100–9. doi: 10.1016/j.jclinepi.2023.06.011. PubMed DOI PMC
White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med. 2011;30:377–99. doi: 10.1002/sim.4067. PubMed DOI
von Hippel PT. 8. How to Impute Interactions, Squares, and other Transformed Variables. Sociol Methodol. 2009;39:265–91. doi: 10.1111/j.1467-9531.2009.01215.x. DOI
Floden L, Bell ML. Imputation strategies when a continuous outcome is to be dichotomized for responder analysis: a simulation study. BMC Med Res Methodol. 2019;19:161. doi: 10.1186/s12874-019-0793-x. PubMed DOI PMC
Gaffert P, Meinfelder F, Bosch V. Bamberg, Germany: 2016. Towards an mi-proper predictive mean matching.
Rässler S, Rubin DB, Zell ER. Imputation. WIREs Computational Stats. 2013;5:20–9. doi: 10.1002/wics.1240. DOI
Machado P, Navarro-Compán V, Landewé R, et al. Calculating the ankylosing spondylitis disease activity score if the conventional c-reactive protein level is below the limit of detection or if high-sensitivity c-reactive protein is used: an analysis in the DESIR cohort. Arthritis Rheumatol . 2015;67:408–13. doi: 10.1002/art.38921. PubMed DOI