Tackling missing radiographic progression data: multiple imputation technique compared with inverse probability weights and complete case analysis
Authors:
- Raimon SanmartíRSRaimon Sanmartí
- Rodrigo Jiménez-GarciaRJRodrigo Jiménez-Garcia
- Loreto CarmonaLCLoreto Carmona
- on behalf of SERAP Study GroupOGon behalf of SERAP Study Group
Open Access
- 29 September 2012
- journal article
- research article
- Published by Oxford University Press (OUP) in Rheumatology
- Vol. 52 (2) , 331-336
Abstract
Objective. To describe the results of different statistical ways of addressing radiographic outcome affected by missing data—multiple imputation technique, inverse probability weights and complete case analysis—using data from an observational study.Keywords
This publication has 19 references indexed in Scilit:
- The additive value of magnetic resonance imaging for bone edema in predicting rheumatoid arthritis development in early undifferentiated arthritis: Comment on the article by Duer-Jensen et alArthritis & Rheumatism, 2011
- Multiple imputation for missing data in epidemiological and clinical research: potential and pitfallsBMJ, 2009
- How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation TheoryPrevention Science, 2007
- Multiple imputation: current perspectivesStatistical Methods in Medical Research, 2007
- EULAR recommendations for the management of early arthritis: report of a task force of the European Standing Committee for International Clinical Studies Including Therapeutics (ESCISIT)Annals of the Rheumatic Diseases, 2007
- Statistical methods inArthritis & Rheumatism: Current trendsArthritis & Rheumatism, 2006
- How to assess early rheumatoid arthritis in daily clinical practiceBest Practice & Research Clinical Rheumatology, 2005
- Model Selection in Estimating EquationsBiometrics, 2001
- Marginal Structural Models and Causal Inference in EpidemiologyEpidemiology, 2000
- Inference and missing dataBiometrika, 1976