Missing data in a multi-item instrument were best handled by multiple imputation at the item score level

• Published in: Journal of clinical epidemiology Vol. 67; no. 3; pp. 335 - 342
• Main Authors: Eekhout, Iris, de Vet, Henrica C.W., Twisk, Jos W.R., Brand, Jaap P.L., de Boer, Michiel R., Heymans, Martijn W.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.03.2014; Elsevier; Elsevier Limited
• Subjects: Algorithms; Analysis. Health state; Back pain; Bias; Biological and medical sciences; Computer Simulation; Data Interpretation, Statistical; Epidemiology; General aspects; Humans; Internal Medicine; Inventory; Item imputation; Linear Models; Medical research; Medical sciences; Methodology; Methods; Missing data; Multi-item questionnaire; Multiple imputation; Public health. Hygiene; Public health. Hygiene-occupational medicine; Research Design; Simulation; Software; Studies; Missing data; Simulation; Bias; Multi-item questionnaire; Multiple imputation; Item imputation; Methods; Multiple; Questionnaire; Methodology; Method; Epidemiology; Medicine; Score; Public health

Regardless of the proportion of missing values, complete-case analysis is most frequently applied, although advanced techniques such as multiple imputation (MI) are available. The objective of this study was to explore the performance of simple and more advanced methods for handling missing data in...