The need to use rigorous, transparent, clearly interpretable, and scientifically justified methodology for preventing and dealing with missing data in clinical trials has been a focus of much attention from regulators, practitioners, and academicians over the past years. New guidelines and recommendations emphasize the importance of minimizing the amount of missing data and carefully selecting primary analysis methods on the basis of assumptions regarding the missingness mechanism suitable for the study at hand, as well as the need to stress-test the results of the primary analysis under different sets of assumptions through a range of sensitivity analyses. Some methods that could be effectively used for dealing with missing data have not yet gained widespread usage, partly because of their underlying complexity and partly because of lack of relatively easy approaches to their implementation. In this paper, we explore several strategies for missing data on the basis of pattern mixture models that embody clear and realistic clinical assumptions. Pattern mixture models provide a statistically reasonable yet transparent framework for translating clinical assumptions into statistical analyses. Implementation details for some specific strategies are provided in an Appendix (available online as Supporting Information), whereas the general principles of the approach discussed in this paper can be used to implement various other analyses with different sets of assumptions regarding missing data.

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Objectives: Stage 1: To evaluate the safety and efficacy of candidate agents as add-on therapies to standard of care (SoC) in patients hospitalised with COVID-19 in a screening stage. Stage 2: To confirm the efficacy of candidate agents selected on the basis of evidence from Stage 1 in patients hospitalised with COVID-19 in an expansion stage. Trial design: ACCORD is a seamless, Phase 2, adaptive, randomised controlled platform study, designed to rapidly test candidate agents in the treatment of COVID-19. Designed as a master protocol with each candidate agent being included via its own sub-protocol, initially randomising equally between each candidate and a single contemporaneous SoC arm (which can adapt into 2:1). Candidate agents currently include bemcentinib, MEDI3506, acalabrutinib, zilucoplan and nebulised heparin. For each candidate a total of 60 patients will be recruited in Stage 1. If Stage 1 provides evidence of efficacy and acceptable safety the candidate will enter Stage 2 where a total of approximately 126 patients will be recruited into each study arm sub-protocol. Enrollees and outcomes will not be shared across the Stages; the endpoint, analysis and sample size for Stage 2 may be adjusted based on evidence from Stage 1. Additional arms may be added as new potential candidate agents are identified via candidate agent specific sub-protocols.

Recent research has fostered new guidance on preventing and treating missing data, most notably the landmark expert panel report from the National Research Council (NRC) that was commissioned by FDA. One of the findings from that panel was the need for better software tools to conduct missing data sensitivity analyses and frameworks for drawing inference from them. In response to the NRC recommendations, a Scientific Working Group was formed under the Auspices of the Drug Information Association (DIASWG). The present paper is from work of the DIASWG. Specifically, the NRC panel's 18 recommendations are distilled into 3 pillars for dealing with missing data: (1) providing clearly stated objectives and causal estimands; (2) preventing as much missing data as possible; and (3) combining a sensible primary analysis with sensitivity analyses to assess robustness of inferences to missing data assumptions. Sample data sets are used to illustrate how sensitivity analyses can be used to assess robustness of inferences to missing data assumptions. The suite of software tools used to conduct the sensitivity analyses are freely available for public use at www.missingdata.org.uk.

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