Layman’s Abstract for Statistics in Medicine article: Defining estimands in clinical trials: A unified procedure

Each week, we publish lay abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.
 
The article featured today is from Statistics in Medicine and is now available to read in full here.
 
Han, SZhou, X-HDefining estimands in clinical trials: A unified procedureStatistics in Medicine20231– 19. doi: 10.1002/sim.9702

 

Estimands are the targeted quantities of clinical trials. To define an estimand, one needs to describe four attributes, including the treatment conditions, the targeted population, the outcome of interest, and the feature of outcome in the study population (e.g., the difference in means). Moreover, since intercurrent events could affect the definitions of these attributions, it is essential to describe how to account for intercurrent events in defining estimands. 

The ICH E9 (R1) addendum proposes five strategies to define estimands by addressing intercurrent events. However, the mathematical forms of these targeted quantities still need to be clarified. The lack of mathematical forms might lead to discordance between statisticians who estimate these quantities and clinicians, drug sponsors, and regulators who interpret them.  

To improve the concordance, Han and Zhou provide a unified four-step procedure for constructing the mathematical estimands. They apply the procedure for each strategy to derive the mathematical estimands and compare the five strategies in practical interpretations, data collection, and analytical methods. Finally, they apply the procedure in two real clinical trials and employ different strategies to address different intercurrent events. In one trial, they consider strategies affecting different attributes of estimands, and in the other, they consider strategies targeting the same attribute. Their examples show that the procedure can ease defining estimands in varying settings with multiple strategies. Their study adds to improve practical guidance on defining estimands and will inform proper design and interpretation of clinical trials. 

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