Imputing missing data of function and disease activity in rheumatoid arthritis registers: what is the best technique?
DAS28
disease activity
epidemiology
outcomes research
rheumatoid arthritis
Journal
RMD open
ISSN: 2056-5933
Titre abrégé: RMD Open
Pays: England
ID NLM: 101662038
Informations de publication
Date de publication:
2019
2019
Historique:
received:
24
04
2019
revised:
02
09
2019
accepted:
20
09
2019
entrez:
2
11
2019
pubmed:
2
11
2019
medline:
2
11
2019
Statut:
epublish
Résumé
To compare several methods of missing data imputation for function (Health Assessment Questionnaire) and for disease activity (Disease Activity Score-28 and Clinical Disease Activity Index) in rheumatoid arthritis (RA) patients. One thousand RA patients from observational cohort studies with complete data for function and disease activity at baseline, 6, 12 and 24 months were selected to conduct a simulation study. Values were deleted at random or following a predicted attrition bias. Three types of imputation were performed: (1) methods imputing forward in time (last observation carried forward; linear forward extrapolation); (2) methods considering data both forward and backward in time (nearest available observation-NAO; linear extrapolation; polynomial extrapolation); and (3) methods using multi-individual models (linear mixed effects cubic regression-LME3; multiple imputation by chained equation-MICE). The performance of each estimation method was assessed using the difference between the mean outcome value, the remission and low disease activity rates after imputation of the missing values and the true value. When imputing missing baseline values, all methods underestimated equally the true value, but LME3 and MICE correctly estimated remission and low disease activity rates. When imputing missing follow-up values at 6, 12, or 24 months, NAO provided the least biassed estimate of the mean disease activity and corresponding remission rate. These results were not affected by the presence of attrition bias. When imputing function and disease activity in large registers of active RA patients, researchers can consider the use of a simple method such as NAO for missing follow-up data, and the use of mixed-effects regression or multiple imputation for baseline data.
Identifiants
pubmed: 31673410
doi: 10.1136/rmdopen-2019-000994
pii: rmdopen-2019-000994
pmc: PMC6802981
doi:
Types de publication
Comparative Study
Journal Article
Observational Study
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
e000994Informations de copyright
© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: DM: none declared for this work. KL: none declared for this work. KP: received honoraria for lectures: AbbVie, Roche, MSD, UCB, Pfizer, Amgen, Egis, BMS. CT: none declared for this work. MJS: none declared for this work. EKK: none declared for this work. AF: none declared for this work. DC: none declared for this work.
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