Comparison of alternative strategies for analysis of longitudinal trials with dropouts

Patients may withdraw from longitudinal clinical trials for many reasons. Methods for handling the problem presented by missing data of patients who withdraw before reaching the time point of the primary measurement include carrying the last observation forward (LOCF), data as observed analysis (DAO), mixed model approaches, and pattern mixture models.

We evaluate a multiple imputation (MI) approach that has the flexibility to adjust inferences about the treatment effect for the withdrawn patients relative to currently used alternatives. Sensitivity analyses are performed under a collection of scenarios that include many circumstances that may arise in practice, including different assumptions about treatment effects post-withdrawal and about the missing data mechanism. Simulations are used to compare the results of analyses based on the MI approach with those based on the LOCF, DAO, and the mixed model approaches.

The LOCF and DAO approaches cannot be recommended as strategies for handling missing responses, at least for these scenarios, because they provide biased estimates of treatment effects and biased tests of the null hypothesis of no treatment effect. Application of the various approaches to the analysis of clinical data from a longitudinal trial confirms the underestimation of the variability when the LOCF approach is used.