SwePub
Sök i LIBRIS databas

  Utökad sökning

WFRF:(Pavelka Karel)
 

Sökning: WFRF:(Pavelka Karel) > Imputing missing da...

Imputing missing data of function and disease activity in rheumatoid arthritis registers : What is the best technique?

Mongin, Denis (författare)
Geneva University Hospital
Lauper, Kim (författare)
Geneva University Hospital
Turesson, Carl (författare)
Lund University,Lunds universitet,Internmedicin - epidemiologi,Forskargrupper vid Lunds universitet,Internal Medicine - Epidemiology,Lund University Research Groups,Skåne University Hospital
visa fler...
Hetland, Merete Lund (författare)
University of Copenhagen,Copenhagen University Hospital
Klami Kristianslund, Eirik (författare)
Diakonhjemmet Hospital
Kvien, Tore K. (författare)
Diakonhjemmet Hospital
Santos, Maria Jose (författare)
Hospital Garcia de Orta
Pavelka, Karel (författare)
Charles University in Prague
Iannone, Florenzo (författare)
Bari University Hospital
Finckh, Axel (författare)
Geneva University Hospital
Courvoisier, Delphine Sophie (författare)
Geneva University Hospital
visa färre...
 (creator_code:org_t)
2019-10-17
2019
Engelska.
Ingår i: RMD Open. - : BMJ. - 2056-5933. ; 5:2
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Reumatologi och inflammation (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Rheumatology and Autoimmunity (hsv//eng)

Nyckelord

DAS28
disease activity
epidemiology
outcomes research
rheumatoid arthritis

Publikations- och innehållstyp

art (ämneskategori)
ref (ämneskategori)

Hitta via bibliotek

  • RMD Open (Sök värdpublikationen i LIBRIS)

Till lärosätets databas

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy