SAT0587 THE ART OF IMPUTING MISSING DATA OF DISEASE AND FUNCTION ACTIVITY IN RHEUMATOID ARTHRITISREGISTRIES
Authors: Denis Mongin, Kim Lauper, Carl Turesson, Merete L. Hetland, Eirik Kristianslund, Tore K. Kvien, Maria Jose Santos, Karel Pavelka, Florenzo Iannone, Axel Finckh, Delphine Courvoisier
Journal: Annals of the Rheumatic Diseases (2019)

<h3>Background</h3> Large observational studies become more common in rheumatoid arthritis (RA). Disease registers [1] allow to analyse the effectiveness and safety of RA treatments in real-world populations, but observational studies suffer from missing data. To minimise bias, it has been shown that imputing missing data is superior to the use of complete case analysis [2]. Although some imputation methods have been studied in clinical trials of rheumatic diseases [3] and in small registers [4], the various imputation techniques have never been systematically compared in large registers. <h3>Objectives</h3> To compare the effects of available imputation methods on the estimated values and on RA remission rate for missing disease activity measures in large registers. <h3>Methods</h3> We used 1000 patients with complete data for disease activity (Disease Activity Score (DAS28) and Clinical Disease Activity Index (CDAI)) at baseline (treatment initiation), 6, 12, and 24 months after initiation of abatacept or a tumor-necrosis factor inhibitor (TNFi) from an existing register collaboration (PANABA). Simulation procedure: Values were deleted randomly and imputed with three types of imputation methods: (1) methods imputing forward in time, such as Last Observation Carried Forward (LOCF) or Linear Forward Extrapolation (LFE); (2) methods considering data both forward and backward in time, such as Nearest Available Observation (NAO), Linear Extrapolation (LE) or Polynomial Extrapolation (PE); and (3) methods using computer-intensive multi-individual imputations, such as Linear Mixed Effects cubic regression (LME3) and Multiple Imputation by Chained Equation (MICE). We conducted a simulation study by performing this procedure 1000 time and computing the mean difference between the true and the imputed values, and between the true remission rate (CDAI and DAS28) and the imputed ones. <h3>Results</h3> Results are summarised in Fig. 1. At baseline, all methods underestimated the true values by at least 20%. Despite this, LME3 and MICE were able to provide estimates of baseline remission rates with less than 3% of error. For follow-up data, missingness at 6, 12, or 24 months, NAO, LE and PE led to relative bias of the mean values ≤15%, and almost unbiased remission rate. LOCF and LFE respectively over and under-estimated the mean imputed values up to 20%, leading respectively to a non-negligible under and over-estimation of the remission rate. Although LME3 and MICE had low bias in estimating the mean values, they narrowed the distribution of the imputed values and thus strongly underestimated remission rate <h3>Conclusion</h3> When imputing disease activity in rheumatoid arthritis register data, researchers should prefer Linear Mixed Effects cubic regression (LME3) for baseline and nearest available observation (NAO) for follow-up data. <h3>References</h3> [1] Finckh A, Courvoisier D. Joint Bone Spine2018;85:271–4. doi:10.1016/j.jbspin.2017.12.005 [2] van der Heijden GJMG, T. Donders AR, Stijnen T, et al. Journal of Clinical Epidemiology2006;59:1102–9. doi:10.1016/j.jclinepi.2006.01.015 [3] van der Heijde D, Fleischmann R, Wollenhaupt J, et al. Ann Rheum Dis2014;73:233–7. doi:10.1136/annrheumdis-2013-203697 [4] Descalzo MÁ, Garcia VV, González-Alvaro I, et al. Rheumatology (Oxford)2013;52:331–6. doi:10.1093/rheumatology/kes245 <h3>Disclosure of Interests</h3> Denis Mongin: None declared, Kim Lauper Grant/research support from: reports an unrestricted research grant from AbbVie and non-financial support from Pfizer, outside the submitted work, Carl Turesson: None declared, Merete L. Hetland Grant/research support from: BMS, MSD, AbbVie, Roche, Novartis, Biogen, Pfizer, Consultant for: Eli Lilly, Speakers bureau: Orion Pharma, Biogen, Pfizer, CellTrion, Merck, Samsung Bioepis, Eirik kristianslund: None declared, Tore K. Kvien Grant/research support from: AbbVie, BMS, MSD, Pfizer, Roche and UCB., Consultant for: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Celltrion, Eli Lilly, Hospira, Merck-Serono, MSD, Novartis, Oktal, Orion Pharma, Pfizer, Roche, Sandoz, Sanofi, Mylan and UCB, Speakers bureau: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Celltrion, Eli Lilly, Hospira, Merck-Serono, MSD, Novartis, Oktal, Orion Pharma, Pfizer, Roche, Sandoz, Sanofi and UCB, Maria Jose Santos: None declared, Karel Pavelka: None declared, Florenzo Iannone Consultant for: F Iannone has received consultancy fees and/or speaker honoraria from Pfizer, AbbVie, MSD, BMS, Novartis, Lilly, UCB outside this work, Speakers bureau: F Iannone has received consultancy fees and/or speaker honoraria from Pfizer, AbbVie, MSD, BMS, Novartis, Lilly, UCB outside this work, Axel Finckh Grant/research support from: Bristol-Myers Squibb, Pfizer Inc, Consultant for: AbbVie, A2Bio, Bristol-Myers Squibb, MSD, Roche, Pfizer Inc, and UCB, Delphine Courvoisier Grant/research support from: has received an unrestricted grant from MSD for this study, Consultant for: has received consulting fees from BMS, Pfizer, AB2 Bio and Janssen., Paid instructor for: Janssen