Aligning Estimators With Estimands in Clinical Trials: Putting the ICH E9(R1) Guidelines Into Practice.

The draft ICH E9(R1) addendum stipulates that an estimator should align with its associated estimand and yield an estimate that facilitates reliable interpretations. The addendum further stipulates that assumptions should be justifiable and plausible, and that the extent of assumptions is an important consideration for whether an estimate will be robust because assumptions are often unverifiable. The draft addendum specifies 5 strategies for dealing with intercurrent events. The intent of this paper is to provide conceptual considerations and technical details for various estimators that align with these strategies. We include focus on how the nature and extent of assumptions influences the potential robustness of the various estimators. The content reflects the knowledge, experience, and opinions of the Drug Information Association's Scientific Working Group on Missing Data. This group includes experienced statisticians from across industry and academia, primarily in the US and European Union.

Recent Developments in the Prevention and Treatment of Missing Data.

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.