Читать книгу Evidence in Medicine - Iain K. Crombie - Страница 32

Instead of ignoring the problem, as complete case analysis does, a better approach is to use a method to estimate what the missing value might be. This is termed imputation. The simplest form of imputation is last observation carried forward. If trials measure the outcome at more than one time point, the most recent observation is used in the analysis; if there are no intermediate measures the baseline measurement is used. This method is often used in trials [45, 48], but it has been heavily criticised because it is likely to lead to biased estimates of treatment effects [55–57]. As disease severity often changes over time, with relapses or remissions, early values can be poor predictors of the final outcome.

Multiple imputation is a more sophisticated approach to estimate the missing outcome data. It uses statistical modelling to derive estimates of the missing data based on the available data (imputation). The modelling includes the variables that would normally be used in the final analysis, such as the stratification variables (e.g. centre, gender), treatment group and potential confounders (e.g. initial disease severity, other medical conditions). It can also include other variables (auxiliary variables) that might be available [58, 59]. The idea is that patient characteristics, both medical and demographic, could predict the final outcome. The modelling process is repeated many times to produce an average result (hence multiple imputation).

Multiple imputation makes an assumption about the nature of the missingness of the data. Termed ‘missing at random’, the assumption is that the missing outcomes can be predicted from the other data in the study [60]. It is recommended that sensitivity analysis should be used to explore the effect of assumptions about the missing data [59]. Although not a perfect solution, multiple imputation is better than other methods of dealing with missing data (such as complete case analysis or last observation carried forward) [58].