Estimands in clinical trials - broadening the perspective

Stat Med. 2017 Jan 15;36(1):5-19. doi: 10.1002/sim.7033. Epub 2016 Jul 19.


Defining the scientific questions of interest in a clinical trial is crucial to align its planning, design, conduct, analysis, and interpretation. However, practical experience shows that oftentimes specific choices in the statistical analysis blur the scientific question either in part or even completely, resulting in misalignment between trial objectives, conduct, analysis, and confusion in interpretation. The need for more clarity was highlighted by the Steering Committee of the International Council for Harmonization (ICH) in 2014, which endorsed a Concept Paper with the goal of developing a new regulatory guidance, suggested to be an addendum to ICH guideline E9. Triggered by these developments, we elaborate in this paper what the relevant questions in drug development are and how they fit with the current practice of intention-to-treat analyses. To this end, we consider the perspectives of patients, physicians, regulators, and payers. We argue that despite the different backgrounds and motivations of the various stakeholders, they all have similar interests in what the clinical trial estimands should be. Broadly, these can be classified into estimands addressing (a) lack of adherence to treatment due to different reasons and (b) efficacy and safety profiles when patients, in fact, are able to adhere to the treatment for its intended duration. We conclude that disentangling adherence to treatment and the efficacy and safety of treatment in patients that adhere leads to a transparent and clinical meaningful assessment of treatment risks and benefits. We touch upon statistical considerations and offer a discussion of additional implications. Copyright © 2016 John Wiley & Sons, Ltd.

Keywords: adherence; causal inference; effectiveness; efficacy; estimand; intention-to-treat; treatment-policy.

MeSH terms

  • Clinical Trials as Topic / standards*
  • Clinical Trials as Topic / statistics & numerical data*
  • Data Interpretation, Statistical
  • Drug Design*
  • Drug Industry / standards*
  • Humans
  • Intention to Treat Analysis
  • Models, Statistical*
  • Research Design