TITLE: Statistical Analyses Targeting Estimands
SPEAKERS: Frank Bretz and Dong Xi, Novartis


Defining the scientific questions of interest in a clinical trial is crucial to align its design, conduct, analysis, and interpretation. With the recent release of the ICH E9(R1) guideline, regulatory agencies require statistical analyses to be aligned with the target estimand(s) which precisely describe the treatment effect(s) of interest that a clinical trial should address. For a given estimand, an aligned method of analysis, or estimator, should be implemented that is able to provide an estimate on which reliable interpretation can be based and which includes the handling of post-randomization events, missing data and sensitivity analyses. Many statistical analysis procedures are available for different types of data, although it is often unclear which estimands these imply. In this tutorial, we discuss how to identify and implement analyses approaches as well as sensitivity analyses that are aligned with a chosen estimand for different types of endpoints (continuous, binary, time-to-event, recurrent events) in longitudinal clinical trial settings. We illustrate the methods with real case studies and provide code examples to facilitate implementation in practice.

Instructors’ Biography:

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. He currently holds 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’ and currently serves on the ICH E20 Expert Working Group on ‘Adaptive clinical trials’. He is a Fellow of the American Statistical Association.



Dong Xi is Associate Director in Advanced Methodology and Data Science group at Novartis. He has supported development and implementation of innovative statistical methodologies in multiple comparisons, dose finding, group sequential designs, estimands and causal inference. He has co-authored four book chapters on multiplicity and many publications in peer-reviewed journals. He is an associate editor of Statistics in Biopharmaceutical Research and Contemporary Clinical trials, and he is a committee member of the International Conference of Multiple Comparison Procedures. His work won the biennial (2019-2020) “Best Paper Award” for manuscripts published in Statistics in Biopharmaceutical Research.

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