Causal Inference and Estimands in Clinical Trials

Abstract

The National Research Council’s report on the prevention and treatment of missing data highlighted the need to clearly specify causal estimands. This focus fundamentally changed how the missing data problem was perceived and addressed in clinical trials. The recent ICH E9(R1) addendum is another major step in promoting the use of the causal estimands framework that should further influence how clinical trial protocols and statistical analysis plans are written and implemented. The language of potential outcomes that is widely accepted in the causal inference literature is not widely recognized in the clinical trialists community and was not used in defining causal estimands in the NRC report or the ICH E9(R1). In this article, we attempt to bridge the gap between the causal inference community and clinical trialists to further advance the use of causal estimands in clinical trial settings. We illustrate how concepts from causal literature, such as potential outcomes and dynamic treatment regimens, can facilitate defining and implementing causal estimands and may provide a unifying language to describing the targets for both observational and randomized clinical trials.

Keywords:

• Causal inference
• Estimands
• Intercurrent events
• Potential outcomes

Acknowledgments

The authors would like to thank for encouragement, discussions, and insightful comments members of the DIA working group on “Tools for missing data”: Michael O’Kelly, Mouna Akacha, Jonathan Bartlett, Devan Mehrotra, and Pritibha Singh. We are very grateful to Stephen Ruberg, Xiang Zhang, two anonymous reviewers, and the associate editor for their valuable comments.