[Roaming through methodology. XVI. What to do about missing data].
Medical scientific research involving multiple measurements in patients is usually complicated by missing values. In case of missing values the choice is to limit the analysis to the complete cases or to analyse all available data. Both methods may suffer from substantial bias and may only be applied in a valid way if the rather strong assumption of 'missing completely at random' holds for the missing values, i.e. the missing value is not related to the other measured data nor to unmeasured data. Two other statistical methods may be applied to deal with missing values: the likelihood approach and the multiple imputation method. These methods make efficient use of all available data and take into account information implied by the available data. These methods are valid under the less stringent assumption of 'missing at random', i.e. the missing value is related to the other measured data, but not to unmeasured data. The best approach is to ensure that no data are missing.
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Erasmus Universiteit, faculteit Geneeskunde en Gezondheidsweten-schappen, Instituut Epidemiologie en Biostatistiek, Rotterdam. stijnen@epib.fgg.eur
Arends
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D002986:Clinical Trials as Topic / Q000379:methods* / Q000592:standards; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D012106:Research / Q000592:standards; D012107:Research Design