Implementation of ICH E9 (R1): a few points learned during the COVID-19 pandemic

TL;DR

This paper discusses how the COVID-19 pandemic challenges clinical trial estimands, proposing improved strategies for handling intercurrent events using causal inference, to better align statistical analysis with clinical objectives.

Contribution

It introduces a refined framework for applying ICH E9 (R1) estimands, emphasizing differential handling of intercurrent events and the use of hypothetical strategies during disruptions like a pandemic.

Findings

Proposes a mix of strategies for handling ICEs based on reasons.

Suggests prioritizing hypothetical strategies for ICEs.

Provides a roadmap for estimation and sensitivity analyses.

Abstract

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.