Imputation of missing data in clinical trials
'''Planning stage of the clinical trial''' To reduce the risks of missing data in the panning of the clinical trial, statistical analyses should be specified, key data items should be identified, and the procedures to prevent missing data '"`UNIQ--ref-0000000D-QINU`"'. '''Analysis stage''' There are different strategies to deal with missing data that will depend on the specific clinical trial and type of missing data: 1. Complete cases analysis could be used when the proportions of missing data are below 5%and the potential impact of the missing data is negligible '"`UNIQ--ref-0000000E-QINU`"'. 2. Single imputation replaces missing values by a value defined by a certain rule. However, this method ignores the data variation and can potentially introduce bias and should be used with great caution '"`UNIQ--ref-0000000F-QINU`"'. 3. When the missing data accomplish certain characteristics, multiple imputation may be used to minimize bias . Missing values are replaced by a random sample of plausible values imputations. There are several multiple imputation methodologies that must be chosen according to the variable with missing values '"`UNIQ--ref-00000010-QINU`"'. To conclude, handling missing data validly is an important, yet difficult and complex, task. This theme showed different strategies to handle missing data but always statistical expertise’s advice is needed. '"`UNIQ--references-00000011-QINU`"'
Missing data are unavoidable in clinical trials. Frequently, complete cases analysis is used only including individuals with no missing data '"`UNIQ--ref-00000009-QINU`"'. However, that can generate bias and can lead to exclude several individuals, causing loss of precision and power '"`UNIQ--ref-0000000A-QINU`"'. The risk of bias from missing data depends on the cause '"`UNIQ--ref-0000000B-QINU`"': Missing completely at random: There are no systematic differences between the missing values and the observed values. Missing at random: Any systematic difference between the missing values and the observed values can be explained by differences in observed data. Missing not at random: Systematic differences remain between the missing values and the observed values. The determination of the type of missing values is difficult due to the nature of missing values '"`UNIQ--ref-0000000C-QINU`"'. Therefore, practical guidelines are needed to deal with missing data.
Missing data is always a limitation in the interpretation of clinical trial results. This missing data may seriously affect the inference from clinical trials. Therefore, it is necessary to establish the criteria to handle missing data prior to the clinical trial. Imputation is a potential tool to overcome the bias from missing data but it must be carefully used.
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