Missing values occur when no data value is stored for a variable of interest in a study observation. In most study datasets there are missing values, and the amount and type of missing values can have an important impact on the conclusions that can be drawn from the data. If the likelihood of data being missing is related a. to the study outcome or b. to other explanatory variables, then the study results will be biased. For example, if the prevalence of a health condition is related to age and older people are less likely to report whether they have the condition (a) then the study will under-report the prevalence of the condition.  If older people are less likely to provide their age (b), then the study will under-report the relationship between the health condition and age. Data are classified as ‘missing completely at random’ (MCAR) if the chances of them being missing is independent of observable variables and of unobservable parameters of interest. In this case the analysis will not be biased (but will be more uncertain). Data are (somewhat confusingly) called ‘missing at random’ (MAR) if the chances of them being missing is not associated directly with the outcome of interest but may be accounted for by variables for which there is complete information. Otherwise missing data are ‘missing not at random’ (MNAR). For MNAR and MAR it is important to understand the patterns of missingness and to use appropriate statistical techniques to control for possible bias (in the case of MAR some statistical analyses may be unbiased). Simple exclusion of study subjects with missing values will usually maintain or increase the bias in the results. Typical methods used to adjust for missing values include imputation (various methods exist), partial deletion, inverse propensity weighting or more complicated maximum likelihood estimation. In economic modelling, sensitivity analysis may be used to explore the impact of ‘best case’ and ‘worst case’ assumptions about missing data in source studies.

How to cite: Missing Values [online]. (2016). York; York Health Economics Consortium; 2016. https://yhec.co.uk/glossary/missing-values/


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