Imputing missing data of function and disease activity in rheumatoid arthritis registers: what is the best technique?
Jazyk angličtina Země Velká Británie, Anglie Médium electronic-ecollection
Typ dokumentu srovnávací studie, časopisecké články, pozorovací studie, práce podpořená grantem
PubMed
31673410
PubMed Central
PMC6802981
DOI
10.1136/rmdopen-2019-000994
PII: rmdopen-2019-000994
Knihovny.cz E-zdroje
- Klíčová slova
- DAS28, disease activity, epidemiology, outcomes research, rheumatoid arthritis,
- MeSH
- algoritmy MeSH
- indukce remise MeSH
- interpretace statistických dat * MeSH
- kohortové studie MeSH
- lidé MeSH
- lineární modely MeSH
- následné studie MeSH
- počítačová simulace MeSH
- revmatoidní artritida epidemiologie MeSH
- stupeň závažnosti nemoci MeSH
- výzkumný projekt statistika a číselné údaje MeSH
- zkreslení výsledků (epidemiologie) MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- pozorovací studie MeSH
- práce podpořená grantem MeSH
- srovnávací studie MeSH
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Centre for Rheumatology and Spine Diseases Rigshospitalet Glostrup Glostrup Denmark
Department of Clinical Medicine University of Copenhagen Copenhagen Denmark
Department of Internal Medicine Lund University Lund Sweden
Department of Rheumatology Diakonhjemmet Hospital Oslo Norway
Department of Rheumatology Hospital Garcia de Orta Almada Portugal
Department of Rheumatology Skåne University Hospital Malmö Sweden
Division of Rheumatology Geneva University Hospitals Geneva Switzerland
Institute of Rheumatology and Clinic of Rheumatology Charles University Prague Czech Republic
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