Application of hypothetical strategies in acute pain

• Published in: Pharmaceutical statistics : the journal of the pharmaceutical industry Vol. 23; no. 3; pp. 399 - 407
• Main Authors: Zhong, Jinglin, Petullo, David
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01-05-2024; Wiley Subscription Services, Inc
• Subjects: Clinical trials; composite variable strategy; estimand strategy; hypothetical strategy; ICH E9 (R1); intercurrent event; treatment policy strategy; ICH E9 (R1); composite variable strategy; hypothetical strategy; intercurrent event; estimand strategy; treatment policy strategy
• ISSN: 1539-1604; 1539-1612
• DOI: 10.1002/pst.2359

Since the publication of ICH E9 (R1), “Addendum to statistical principles for clinical trials: on choosing appropriate estimands and defining sensitivity analyses in clinical trials,” there has been a lot of debate about the hypothetical strategy for handling intercurrent events. Arguments against the hypothetical strategy are twofold: (1) the clinical question has limited clinical/regulatory interest; (2) the estimation may need strong statistical assumptions. In this article, we provide an example of a hypothetical strategy handling use of rescue medications in the acute pain setting. We argue that the treatment effect of a drug that is attributable to the treatment alone is the clinical question of interest and is important to regulators. The hypothetical strategy is important when developing non‐opioid treatment as it estimates the treatment effect due to treatment during the pre‐specified evaluation period whereas the treatment policy strategy does not. Two widely acceptable and non‐controversial clinical inputs are required to construct a reasonable estimator. More importantly, this estimator does not rely on additional strong statistical assumptions and is considered reasonable for regulatory decision making. In this article, we point out examples where estimators for a hypothetical strategy can be constructed without any strong additional statistical assumptions besides acceptable clinical inputs. We also showcase a new way to obtain estimation based on disease specific clinical knowledge instead of strong statistical assumptions. In the example presented, we clearly demonstrate the advantages of the hypothetical strategy compared to alternative strategies including the treatment policy strategy and a composite variable strategy.