The estimand framework and its application in substance use disorder clinical trials: a case study
Roydhouse, JK and Floden, L and Tomko, RL and Gray, KM and Bell, ML, The estimand framework and its application in substance use disorder clinical trials: a case study, American Journal of Drug and Alcohol Abuse, 47, (6) pp. 658-663. ISSN 0095-2990 (2021) [Refereed Article]
Relapse rates among individuals with substance use disorder (SUD) remain high and new treatment approaches are needed, which require evaluation in randomized controlled trials (RCTs). Measurement and interpretation challenges for SUD RCT data are often ignored or presented only in statistical analysis plans. Since different analytic approaches may result in different estimates and thus interpretations of the treatment effect, it is important to present this clearly throughout the trial. Inconsistencies between study analyses and objectives present further challenges for interpretation and cross-study comparisons. The recent International Council for Harmonization (ICH) addendum provides standardized language and a common framework for aligning trial objectives, design, conduct, and analysis. The framework focuses on estimands, which describe the treatment effect and link the trial objective with the scientific question and the analytic approach. The use of estimands offers SUD researchers and clinicians the opportunity to explicitly address events that affect measurement and interpretation at the outset of the trial. Furthermore, the use of standard terminology can lead to clearer interpretations of SUD trials and the treatments evaluated in SUD trials. Resources for understanding and applying estimands are needed to optimize the use of this new, helpful framework. This Perspective provides this resource for SUD researchers. Specifically, it highlights the relevance of estimands for SUD trials. Furthermore, it demonstrates how estimands can be used to develop clinically relevant analyses to address challenges in SUD trials. It also shows how a standardized framework can be employed to improve the interpretation and presentation of SUD study findings.