As part of ENAR's education initiative, our webinars promote continuing education for professional and student statisticians by disseminating cutting-edge knowledge to our membership. An ENAR webinar (or "webENAR") can strengthen your background in methodology and software, provide an opportunity to learn about a topic outside of your primary area of specialization, or deepen your understanding of an area in which you already work. We invite you to participate and benefit from the expertise of some of North America's leading statisticians and biostatisticians.
The Webinar Committee of the ENAR Regional Advisory Board (RAB) is coordinating this ongoing series of 1- to 2-hour webinars given by renowned experts. Registration fees are by membership category, with a reduced fee for student members. The webinars are planned to be broadly available and we encourage groups at your institution or workplace to participate together. WebENARs provide excellent learning opportunities for students and professionals alike.
Registration fees are determined by membership category.
*ENAR has decided to waive WebENAR registration fees for current ENAR members during the pandemic. Advance registration is still required for all attendees. Please email email@example.com if you have any questions.
Estimands, Estimators, and Estimates: Aligning Target of Estimation, Method of Estimation, and Sensitivity Analysis, with Application to the COVID-19 Pandemic
Friday, November 19, 2021
10 a.m. to 12 p.m. ET
Bharani Dharan is a Global Group Head, Biostatistics in the Oncology development analytics unit at Novartis Pharmaceuticals, East Hanover. He has managed multiple compounds in late Phase clinical trials in oncology and has experience across multiple disease indications. He has more than 20 years of experience in Pharmaceutical industry. Prior to joining Novartis, he was a project statistician at GlaxoSmithKline. In addition to his current role at Novartis, he also leads the internal cross-functional estimand workstream. His areas of interest include estimands, group sequential designs, adaptive designs and multiplicity.
Frank Bretz is a Distinguished Quantitative Research Scientist at Novartis. He has supported the methodological development in various areas of pharmaceutical statistics, including adaptive designs, dose finding, estimands, and multiple testing. Frank is currently holding adjunct professorial positions at the Hannover Medical School (Germany) and the Medical University of Vienna (Austria). He was a member of the ICH E9(R1) Expert Working Group on 'Estimands and sensitivity analysis in clinical trials.' Frank is a Fellow of the American Statistical Association.
Kelly van Lancker
Johns Hopkins University
Kelly Van Lancker recently obtained her PhD in statistics at Ghent University. At the beginning of September, she started a postdoctoral research position at the Johns Hopkins Bloomberg School of Public Health. Kelly's research focuses on the use of causal inference methods in clinical trials.
Stijn Vansteelandt is an expert in statistical methodology for causal inference. He has authored over 200 peer-reviewed publications in international journals on a variety of topics in biostatistics, epidemiology and medicine, such as the analysis of longitudinal and clustered data, missing data, mediation and moderation/interaction, instrumental variables, family-based genetic association studies, analysis of outcome-dependent samples and phylogenetic inference. He has recently finished a term as Co-Editor of Biometrics, the leading flagship journal of the International Biometrics Society, and has previously served as Associate Editor for the journals Biometrics, Biostatistics, Epidemiology, Epidemiologic Methods and the Journal of Causal Inference. In 2020, he has joined the editorial board of the Journal of the Royal Statistical Society - Series B. His recent work focuses on strategies for obtaining valid inference for statistical and causal effect estimands when the analysis involves data-adaptive methods, such as variable selection or machine learning. Motivated by applications in (personalised) medicine, additional strands of work focus on intercurrent events in clinical trials, and on causal prediction based on electronic health records.
The ICH E9(R1) Addendum on 'Estimands and Sensitivity Analysis in Clinical Trials' introduced a framework to align planning, design, conduct, analysis, and interpretation of clinical trials. When defining the clinical question of interest, clarity is needed about 'intercurrent events' that affect either the interpretation or the existence of the measurements associated with the clinical question of interest, such as discontinuation of assigned treatment, use of an additional or alternative treatment and terminal events such as death. The description of an estimand should reflect the clinical question of interest in respect of these intercurrent events, and the Addendum introduces strategies to reflect different questions of interest that might be posed. The choice of strategies can influence how more conventional attributes of a trial are reflected when describing the clinical question, for example the treatments, population or the variable (endpoint) of interest.
In this seminar we briefly introduce the estimand framework according to the ICH E9(R1) Addendum and describe various strategies for addressing intercurrent events when defining the clinical question of interest. We then reflect on the experience and lessons learned of implementing the Addendum through an internal cross-functional and cross-divisional working group that encompasses various estimand initiatives. Next, we discuss in detail the hypothetical estimand strategy, where a scenario is envisaged in which the intercurrent event would not occur. The Addendum acknowledges that a wide variety of hypothetical scenarios can be envisaged, but it also clarifies that some scenarios are likely to be of more clinical or regulatory interest than others. We share our experiences and try to provide some guidance on their use in clinical trial practice. Finally, we demonstrate how the estimand framework can usefully be applied to clinical trials impacted by the COVID-19 pandemic to address potential pandemic-related trial disruptions and embed them in the context of study objectives and design elements. We introduce different hypothetical estimand strategies and review various causal inference and missing data methods such as multiple imputation and (augmented) inverse probability weighting for the estimation step. To clarify, we describe the features of a stylized trial in neuroscience, and how it may have been impacted by the pandemic. This stylized trial will then be re-visited by discussing the changes to the estimand and the estimator to account for pandemic disruptions.
Webinar recording will be available by 12/3/2021.