Missing data is an ever-present problem in longitudinal clinical trials. Considerable advances in statistical methodology and in our ability to implement those methods have been made in recent years, leading to more choices in analytic approaches for longitudinal data. However, more analytic choice has led to greater complexity in understanding the strengths and limitations of the various approaches, especially since analytic choices are often inextricably linked to choices of endpoints and hypotheses. In drug development, assessing efficacy and effectiveness are both essential. The key is to put the greatest emphasis on the one most appropriate for the given stage of development. Efficacy hypotheses are more closely aligned with the design and goals of confirmatory trials, whereas effectiveness is best tested in more naturalistic settings. Analyses that include dropout as part of the outcome or use data obtained after discontinuation of the study drug may play a role in analysis plans, but they do not provide meaningful solutions to the problem of missing data in the primary analysis of confirmatory trials. Consequently, broad consensus across industry and academia has emerged indicating that the primary analyses of data from confirmatory longitudinal clinical trials should move away from single imputation methods such as last and baseline observation carried forward to analyses such as likelihood-based mixed-effects analyses and multiple imputation. Sensitivity analyses should also be incorporated into analysis plans to specifically assess the robustness of results to key assumptions of the primary analysis.