Questions such as whether to continue to collect trial outcome data after a participant has discontinued use of the study treatment, whether to use a single or composite outcome measure, how long to measure outcome data, all depend on the estimation goal of the trial. This estimation includes not only the outcome of interest, but also whether the focus is on short- or long-term effects of the intervention and the target population of interest. Possibilities for the latter include the “intent-to-treat” population, or the population of treatment compliers. Before selecting a trial design, it is important to decide on the primary parameter and population of interest, the “causal estimand.” Once the estimand is decided, the clinical trial design can be optimized for the measurement of that estimand.

Recommendation 1: The trial protocol should explicitly define (a) the objective(s) of the trial; (b) the associated primary outcome or outcomes; (c) how, when, and on whom the outcome or outcomes will be measured; and (d) the measures of intervention effects, that is, the causal estimands of primary interest. These measures should be meaningful for all study participants, and estimable with minimal assumptions. Concerning the latter, the protocol should address the potential impact and treatment of missing data.


The interpretation of the trial findings is more difficult when participants discontinue their assigned interventions before the end of the study. Therefore, the trial design should be selected to maximize the number of participants who are maintained on the study intervention throughout the duration of the trial.

Recommendation 2: Investigators, sponsors, and regulators should design clinical trials consistent with the goal of maximizing the number of participants who are maintained on the protocol-specified intervention until the outcome data are collected.

There is a key distinction between treatment dropout and analysis dropout, and although there are trials in which treatment dropout will understandably be substantial, there is very little reason for substantial amount of missing data, that is, analysis dropouts. Furthermore, for many trial estimands, the benefits of retaining participants in the study can be substantial, including to support an analysis of effectiveness (comparison

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