Iris Eekhout

VU University Medical Center, Amsterdam, North Holland, Netherlands

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Publications (2)10.84 Total impact

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    ABSTRACT: 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.
    Journal of clinical epidemiology 11/2013; · 5.33 Impact Factor
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    ABSTRACT: The objectives of this systematic review are to examine how researchers report missing data in questionnaires and to provide an overview of current methods for dealing with missing data. We included 262 studies published in 2010 in 3 leading epidemiologic journals. Information was extracted on how missing data were reported, types of missing, and methods for dealing with missing data. Seventy-eight percent of the studies lacked clear information about the measurement instruments. Missing data in multi-item instruments were not handled differently from other missing data. Complete-case analysis was most frequently reported (81% of the studies), and the selectivity of missing data was seldom examined. Although there are specific methods for handling missing data in item scores and in total scores of multi-item instruments, these are seldom applied. Researchers mainly use complete-case analysis for both types of missing, which may seriously bias the study results.
    Epidemiology (Cambridge, Mass.) 05/2012; 23(5):729-32. · 5.51 Impact Factor