Clinical Trials

❘❙❚■ Chapter 30 | Missing DataMultiple imputation is now readily available in public domain software such asSOLAS version 3.0 and above, SAS 8.2 and above, S-Plus, and MICE [15–18].In the analysis of clinical trials, multiple imputation is most useful for dealing withmissing covariates. When it is only the outcome variable that is missing, a correctlyspecified model for all of the available data will give similar results for lesscomputational effort. Multiple imputation is therefore rarely used for imputingoutcome variables in clinical trials.Methods for MNAR dataOf the methods we have described, only a likelihood-based analysis of all the dataand multiple imputation are capable of producing valid, unbiased estimates if themissing data are MAR. However, in practice, it is difficult to be certain that thedata are indeed MAR, and to exclude the possibility that the missingnessmechanism is MNAR. Unfortunately, when the data are MNAR we cannot ignorethe missing data process as with MAR, and none of the approaches we havedescribed will result in unbiased estimates.There have been suggestions for MNAR models – eg, Diggle and Kenward providesuch a modeling procedure for a continuous response [8]. However, such modelsrequire strong and untestable distributional assumptions [8]. A pragmatic approachis to make such models a component of a sensitivity analysis carried out to investigatethe robustness of results obtained using a plausible MAR-based analysis [7,9].Comparison of different strategies for dealing withdifferent types of missing dataIn this section, we will use simulated data to illustrate patterns of missing data andthe consequences of using different analytical strategies.The simulation model for the full datasetConsider a randomized controlled trial with 2,000 participants in each of twotreatment groups (A and B) and four follow-up visits, with the primary outcomebeing the response at the final visit. For simplicity, let us say that the outcomevariable is not measured at baseline. Let us suppose that in group A, at the firstvisit, the true mean level of the variable of interest is 150 units, and that thisincreases by 10 units at each follow-up visit.Furthermore, suppose that in group B the true mean level is 10 units higher thanin group A at each follow-up visit, and that the correlation structure of the344