Kelly van Lancker, Ilya Lipkovich, Martin Ho, Alex Ocampo

Causal Inference in Clinical Trials: To understand the potential of the practical application of causal inference methods in drug development and be able to apply these to real world problems or clinical trials.

As the first installment of this two-part webinar series, this first webinar will provide an introduction to causal inference ideas and methods and how these relate to the estimand framework in both the setting of RCTs or real world data. Graphical methods for communicating causal networks will also be outlined. Please see below the Abstracts for each Guest Presentation. 

The role of causal inference in clinical trials: an introduction
Kelly van Lancker (Ghent University)
In light of the recently published ICH E9(R1) guideline on estimands and sensitivity analysis (2019) and the FDA draft guideline on covariate adjustment (2023), causal inference is progressively taking a more prominent role in the landscape of global drug development. In this talk we will try to bridge the gap between communities by elaborating on how this field provides a convenient, unifying framework, language and relevant tools to formally establish causal relationships. We will hereby illustrate how causal thinking, combined with important tools such as potential outcomes, can facilitate defining, identifying and estimating treatment effects. Building on this, we discuss the role of causal inference in different trial settings, including targeting intention-to-treat effects with covariate adjustment, handling intercurrent events and the incorporation of external control data.

Causal inference and estimands in clinical trials
Ilya Lipkovich (Eli Lilly)
This presentation revisits recent ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials and discusses various strategies for handling intercurrent events (ICEs) using the causal inference framework. The language of potential outcomes (PO) is widely accepted in the causal inference literature but is not yet recognized in the clinical trial community and was not used in defining causal estimands in ICH E9(R1). I will try to bridge the gap between the causal inference community and clinical trialists by advancing the use of causal estimands in clinical trial settings and illustrate how concepts from causal literature, such as POs and dynamic treatment regimens, can facilitate defining and implementing causal estimands for different types of outcomes providing a unifying language for both observational and randomized clinical trials. I emphasize the need for a mix of strategies in handling different types of ICEs, rather than taking one-strategy-fit-all approach and suggest that hypothetical strategies should be used more broadly and provide examples of different hypothetical strategies for different types of ICEs.

A Causal Inference Roadmap for Generating RWE in Regulatory Context: An Introduction and Illustration
Martin Ho (Pfizer)
As real-world data (RWD) become more readily available, the regulatory agencies, medical product developers, and other key stakeholders has increasing interests in exploring the use of real-world evidence (RWE) to support regulatory decisions alternative to traditional clinical trials. To facilitate and promote statistical research in design, analysis, and interpretation of RWE studies for regulatory decision making, the ASA Biopharmaceutical Section established the RWE Scientific Working Group to address challenges and identify opportunities in the statistical research of this area. In a Working Group publication in 2022, Ho and colleagues (DOI 10.1080/19466315.2021.1883475) have proposed a causal inference roadmap for study design and analysis that generates RWE for regulatory considerations. In this talk, Martin will briefly review the steps of the roadmap before using an example to illustrate how to apply the roadmap to generate RWE for regulatory consideration.

Single-World Intervention Graphs for Defining, Identifying, and Communicating Estimands in Clinical Trials
Alex O'Campo (Novartis)
Confusion often arises when attempting to articulate target estimand(s) of a clinical trial in plain language. We aim to rectify this confusion by using a type of causal graph called the Single-World Intervention Graph (SWIG) to provide a visual representation of the estimand that can be effectively communicated to interdisciplinary stakeholders. These graphs not only display estimands, but also illustrate the assumptions under which a causal estimand is identifiable by presenting the graphical relationships between the treatment, intercurrent events, and clinical outcomes. To demonstrate its usefulness in pharmaceutical research, we present examples of SWIGs for various intercurrent event strategies specified in the ICH E9(R1) addendum, as well as an example from a real-world clinical trial for chronic pain. LaTeX code to generate all the SWIGs shown is this paper is made available. We advocate clinical trialists adopt the use of SWIGs in their estimand discussions during the planning stages of their studies.