  The current COVID-19 pandemic poses numerous challenges for ongoing clinical
trials and provides a stress-testing environment for the existing principles
and practice of estimands in clinical trials. The pandemic may increase the
rate of intercurrent events (ICEs) and missing values, spurring a great deal of
discussion on amending protocols and statistical analysis plans to address
these issues. In this article we revisit recent research on estimands and
handling of missing values, especially the ICH E9 (R1) on Estimands and
Sensitivity Analysis in Clinical Trials. Based on an in-depth discussion of the
strategies for handling ICEs using a causal inference framework, we suggest
some improvements in applying the estimand and estimation framework in ICH E9
(R1). Specifically, we discuss a mix of strategies allowing us to handle ICEs
differentially based on reasons for ICEs. We also suggest ICEs should be
handled primarily by hypothetical strategies and provide examples of different
hypothetical strategies for different types of ICEs as well as a road map for
estimation and sensitivity analyses. We conclude that the proposed framework
helps streamline translating clinical objectives into targets of statistical
inference and automatically resolves many issues with defining estimands and
choosing estimation procedures arising from events such as the pandemic.
