As discussed previously, missing data is a common problem in longitudinal studies. Typically, patients who drop out of the study are not comparable to patients who remain in the study at all times. Consequently, the likelihood of dropout is correlated with the underlying unobserved data. To account for this problem, pattern mixture models will also be fit in addition to the previous models that ignore the effect of missing data on resulting estimates. Pattern mixture models are based on fitting separate linear mixed model to each of the seven dropout patterns that can occur in the data such that a separate time trend is estimated for each pattern. The marginal treatment effect is then calculated by a weighted average of the pattern-specific treatment effects, with weights given by the probability of occurrence of the various patterns. Because the estimated marginal treatment effects rely on the extrapolation of fitted average profiles to time points where data is not observed, sensitivity analyses will also be performed to examine the robustness of the results to alternative assumptions. For instance, rather than estimating a separate time trend for each pattern, the model could be simplified so that drop-out pattern is used as a covariate in the model. In this case, the time trend within a pattern is unstructured but parallel across patterns. If estimated marginal treatment effects agree among the different models that are investigated, this will suggest that the results are not artifacts of the particular model used to account for dropout.