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
• ISSN: 0895-4356; 1878-5921; 1878-5921
• DOI: 10.1016/j.jclinepi.2013.09.009

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 cases when some, many, or all item scores are missing in a multi-item instrument. Real-life missing data situations were simulated in a multi-item variable used as a covariate in a linear regression model. Various missing data mechanisms were simulated with an increasing percentage of missing data. Subsequently, several techniques to handle missing data were applied to decide on the most optimal technique for each scenario. Fitted regression coefficients were compared using the bias and coverage as performance parameters. Mean imputation caused biased estimates in every missing data scenario when data are missing for more than 10% of the subjects. Furthermore, when a large percentage of subjects had missing items (>25%), MI methods applied to the items outperformed methods applied to the total score. We recommend applying MI to the item scores to get the most accurate regression model estimates. Moreover, we advise not to use any form of mean imputation to handle missing data.