Choosing estimands in clinical trials with missing data

Abstract

Recent research has fostered new guidance on preventing and treating missing data. Consensus exists that clear objectives should be defined along with the causal estimands; trial design and conduct should maximize adherence to the protocol specified interventions; and a sensible primary analysis should be used along with plausible sensitivity analyses. Two general categories of estimands are effects of the drug as actually taken (de facto, effectiveness) and effects of the drug if taken as directed (de jure, efficacy). Motivated by examples, we argue that no single estimand is likely to meet the needs of all stakeholders and that each estimand has strengths and limitations. Therefore, stakeholder input should be part of an iterative study development process that includes choosing estimands that are consistent with trial objectives. To this end, an example is used to illustrate the benefit from assessing multiple estimands in the same study. A second example illustrates that maximizing adherence reduces sensitivity to missing data assumptions for de jure estimands but may reduce generalizability of results for de facto estimands if efforts to maximize adherence in the trial are not feasible in clinical practice. A third example illustrates that whether or not data after initiation of rescue medication should be included in the primary analysis depends on the estimand to be tested and the clinical setting. We further discuss the sample size and total exposure to placebo implications of including post-rescue data in the primary analysis.