Handling of missing component information for common composite score outcomes used in axial spondyloarthritis research when complete-case analysis is unbiased
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články
PubMed
40021967
PubMed Central
PMC11869558
DOI
10.1186/s12874-025-02515-3
PII: 10.1186/s12874-025-02515-3
Knihovny.cz E-zdroje
- Klíčová slova
- Axial spondyloarthritis, Complete-case analysis, Composite score, Missing components, Multiple imputation,
- MeSH
- axiální spondyloartritida * diagnóza MeSH
- C-reaktivní protein analýza MeSH
- interpretace statistických dat MeSH
- lidé MeSH
- stupeň závažnosti nemoci MeSH
- výzkumný projekt MeSH
- zkreslení výsledků (epidemiologie) MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa MeSH
- Názvy látek
- C-reaktivní protein MeSH
BACKGROUND: Observational data on composite scores often comes with missing component information. When a complete-case (CC) analysis of composite scores is unbiased, preferable approaches of dealing with missing component information should also be unbiased and provide a more precise estimate. We assessed the performance of several methods compared to CC analysis in estimating the means of common composite scores used in axial spondyloarthritis research. METHODS: Individual mean imputation (IMI), the modified formula method (MF), overall mean imputation (OMI), and multiple imputation of missing component values (MI) were assessed either analytically or by means of simulations from available data collected across Europe. Their performance in estimating the means of the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), and the Ankylosing Spondylitis Disease Activity Score based on C-reactive protein (ASDAS-CRP) in cases where component information was set missing completely at random was compared to the CC approach based on bias, variance, and coverage. RESULTS: Like the MF method, IMI uses a modified formula for observations with missing components resulting in modified composite scores. In the case of an unbiased CC approach, these two methods yielded representative samples of the distribution arising from a mixture of the original and modified composite scores, which, however, could not be considered the same as the distribution of the original score. The IMI and MF method are, thus, intrinsically biased. OMI provided an unbiased mean but displayed a complex dependence structure among observations that, if not accounted for, resulted in severe coverage issues. MI improved precision compared to CC and gave unbiased means and proper coverage as long as the extent of missingness was not too large. CONCLUSIONS: MI of missing component values was the only method found successful in retaining CC's unbiasedness and in providing increased precision for estimating the means of BASDAI, BASFI, and ASDAS-CRP. However, since MI is susceptible to incorrect implementation and its performance may become questionable with increasing missingness, we consider the implementation of an error-free CC approach a valid and valuable option. TRIAL REGISTRATION: Not applicable as study uses data from patient registries.
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 Rheumatology Research Landspitali University Hospital Reykjavik Iceland
Department of Medicine Solna Clinical Epidemiology Division Karolinska Institutet Stockholm Sweden
Department of Rheumatology Aarhus University Hospital Aarhus Denmark
Department of Rheumatology Geneva University Hospital Geneva Switzerland
Department of Rheumatology Kuopio University Hospital Kuopio Finland
Department of Rheumatology University Hospital Zurich University of Zurich Zurich Switzerland
Department Rheumatology and Immunology Inselspital University Hospital Bern Bern Switzerland
Faculty of Medicine University of Iceland Reykjavik Iceland
Faculty of Medicine University of Ljubljana Ljubljana Slovenia
Institute of Rheumatology Prague Czech Republic
Rheumatology Radboud University Medical Centre Nijmegen The Netherlands
Sociedade Portuguesa de Reumatologia Reuma Pt Lisbon Portugal
Zobrazit více v PubMed
Garrett S, Jenkinson T, Kennedy LG, Whitelock H, Gaisford P, Calin A. A new approach to defining disease status in ankylosing spondylitis: the Bath Ankylosing Spondylitis Disease Activity Index. J Rheumatol. 1994:21(12):2286–91. PubMed
Calin A, Garrett S, Whitelock H, Kennedy LG, O’hea J, Mallorie P et al. A new approach to defining functional ability in ankylosing spondylitis: the development of the Bath Ankylosing Spondylitis Functional Index. J Rheumatol. 1994:21(12):2281–2285. PubMed
Lukas C, Landewé R, Sieper J, Dougados M, Davis J, Braun J, et al. Development of an ASAS-endorsed disease activity score (ASDAS) in patients with ankylosing spondylitis. Ann Rheum Dis. 2009:68:18–24. PubMed
van der Heijde D, Lie E, Kvien T, Sieper J, Van den Bosch F, Listing J, et al. ASDAS, a highly discriminatory ASAS-endorsed disease activity score in patients with ankylosing spondylitis. Ann Rheum Dis. 2009:68:1811–8. PubMed
Jones GT, Dean LE, Pathan E, Hollick RJ, Macfarlane GJ. Real-word evidence of TNF inhibition in axial spondyloarthritis: can we generalise the results from clinical trials? Ann Rheum Dis. 2020:79:914–9. PubMed
Marzo-Ortega H, Sieper J, Kivitz AJ, Blanco R, Cohen M, Pavelka K, et al. 5-year efficacy and safety of secukinumab in patients with ankylosing spondylitis: end-of-study results from the phase 3 MEASURE 2 trial. Lancet Rheumatol. 2020;2(6):e339–46. PubMed
Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7(2):147. PubMed
Braun J, McHugh N, Singh A, Wajdula JS, Sato R. Improvement in patient-reported outcomes for patients with ankylosing spondylitis treated with etanercept 50 mg once-weekly and 25 mg twice-weekly. Rheumatology. 2007;46(6):999–1004. PubMed
Horton NJ, Kleinman KP. Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. Am Stat. 2007;61(1):79–90. PubMed PMC
Carpenter JR, Kenward MG. Multiple imputation and its application. 1st ed. Hoboken: Wiley & Sons; 2013.
Rombach I, Gray AM, Jenkins C, Murray DW, Rivero-Arias O. Multiple imputation for patient reported outcome measures in randomized controlled trials: advantages and disadvantages of imputing at the item, subscale or composite score level. BMC Med Res Methodol. 2018;18:87. PubMed PMC
O’Keeffe AG, Farewell DM, Tom BDM, Farewell VT. Multiple imputation of missing composite outcomes in longitudinal data. Stat Biosci. 2016;8:310–32. PubMed PMC
Simons CL, Rivero-Arias O, Yu LM, Simon J. Multiple imputation to deal with missing EQ-5D-3L data: should we impute individual domains or the actual index? Qual Life Res. 2015;24(4):805–15. PubMed
White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–99. PubMed
Hughes RA, Heron J, Sterne JAC, Tilling K. Accounting for missing data in statistical analyses: multiple imputation is not always the answer. Int J Epidemiol. 2019;48(4):1294–304. PubMed PMC
Morris TP, White IR, Crowther MJ. Using simulation studies to evaluate statistical methods. Stat Med. 2019;38:2074–102. PubMed PMC
The EuroSpA Research Collaboration Network. URL https://eurospa.eu (2016). Accessed 2 Feb 2023.
Ornbjerg LM, Linde L, Georgiadis S, Rasmussen SH, Lindström U, Askling J, et al. Predictors of ASDAS-CRP inactive disease in axial spondyloarthritis during treatment with TNF-inhibitors: Data from the EuroSpA collaboration. Semin Arthritis Rheum. 2022;56: 152081. PubMed
Van Buuren S. Flexible imputation of missing data. 2nd ed. London: Chapman and Hall/CRC; 2012.
van Buuren S, Groothuis-Oudshoorn K. mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3):1–67.
Kleinke K. Multiple imputation by predictive mean matching when sample size is small. Methodology. 2018;14:3–15.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2022. URL https://www.r-project.org/.
RStudio Team. RStudio: Integrated development for R. RStudio, PBC, Boston, MA. 2020. URL http://www.rstudio.com.
Microsoft Corporation, Weston S. doParallel: foreach parallel adaptor for the ‘parallel’ package. 2022. URL https://cran.r-project.org/package=doParallel.
Microsoft Corporation, Weston S. foreach: provides foreach looping construct. 2022. URL https://cran.r-project.org/package=foreach.
Wickham H. ggplot2: elegant graphics for data analysis. 2nd ed. New York: Springer; 2016.
Ramiro S, van Tubergen A, van der Heijde D, van den Bosch F, Dougados M, Landewé R. How to deal with missing items in BASDAI and BASFI. Rheumatology. 2014;53(2):374–6. PubMed
Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338: b2393. PubMed PMC