The following short courses will be held on Sunday, 3rd of September 2023 (the day before the start of the main conference). Note that main conference registration is a prerequisite to be able to book short courses.

Time Virtual attendance possible? Course Instructors
Full-day No Advanced group-sequential and adaptive confirmatory clinical trial designs, with R practicals using rpact Marcel Wolbers, Kaspar Rufibach, Gernot Wassmer and Marc Vandemeulebroecke
Full-day Yes Bayesian methods for missing covariates in longitudinal studies Nicole Erler and Emmanuel Lesaffre
Morning Yes Implementing the estimand framework in global drug development: Application of causal inference approaches Mouna Akacha, Björn Bornkamp, Alex Ocampo and Jiawei Wei
Morning No Go fastR: High Performance Computing with R Michael Mayer and Lukas Widmer
Afternoon Yes Target Trial Emulation for Causal Inference from Real-World Data Vanessa Didelez and Maria Geers
Afternoon Yes Improving Precision and Power in Randomized Trials by Leveraging Baseline Variables Kelly Van Lancker, Michael Rosenblum and Josh Betz
Afternoon Yes Model and Algorithm Evaluation in Supervised Machine Learning Max Westphal and Rieke Alpers

Advanced group-sequential and adaptive confirmatory clinical trial designs, with R practicals using rpact

Hybrid?: No.
Keywords: interim analyses; sample size re-calculation; multi-arm multi-stage designs

  • Marcel Wolbers, Roche Pharma, Basel, Switzerland
  • Kaspar Rufibach, Roche Pharma, Basel, Switzerland
  • Gernot Wassmer, rpact and University of Cologne, Germany
  • Marc Vandemeulebroecke, Novartis, Basel, Switzerland

This course is intended for biostatisticians from pharma and academia who are interested in learning more about advanced topics in group-sequential and adaptive clinical trial designs. Topics covered are the efficient use of interim analyses in group-sequential trials, an introduction to adaptive trials and sample size recalculation, the use of closed testing procedures for adaptive trials with multiple objectives, and multi-arm multi-stage designs. Examples from real clinical trials will be used throughout the presentations. We also aim to discuss operational aspects of implementing such designs in practice.

The course will be a mix of presentations and practicals using the R package rpact, a free and fully validated package for the design and analysis of group-sequential and adaptive trials. We will assume basic familiarity with group-sequential designs and R. Participants are asked to bring a laptop with R and rpact installed. It is the ambition of the instructors to make this course very interactive.

Bayesian methods for missing covariates in longitudinal studies

Hybrid?: Yes.
Keywords: Bayesian Methods; Missing Data; Longitudinal Data; Imputation

  • Nicole Erler, Erasmus University Medical Center, Rotterdam, the Netherlands
  • Emmanuel Lesaffre, KU Leuven, Belgium

Missing values commonly complicate the analysis of observational data. Multiple imputation (MI) is considered the “gold standard” for handling incomplete covariates. MI, developed at the beginning of the Computer Age, is based on Bayesian ideas. In complex settings, e.g. involving non-linear associations or multi-level data, the assumptions of the commonly used MI algorithms are, however, often violated, leading to possibly biased results. Thanks to the current computational power, a fully Bayesian approach, allowing us to simultaneously estimate parameters of interest and impute missing values, is now feasible. This approach is theoretically valid and superior to MI in complex settings. Highly complex non-standard missing data models can relatively easily be implemented with the help of freely available software such as the R package JointAI. In this course, we briefly review the essentials of multi-level data, Bayesian concepts and (multiple) imputation. The main focus is on the Bayesian approach to missing values in covariates in multi-level and longitudinal studies, which is motivated and illustrated using examples from clinical and epidemiological studies. Practical sessions will be organized to show the capabilities of the R package JointAI, starting with simpler standard settings and extending to highly complex joint models for longitudinal and survival data and imputation in non-standard settings.

Implementing the estimand framework in global drug development: Application of causal inference approaches

Hybrid?: Yes.
Keywords: causal inference; conditional estimand; marginal estimand; standardization; ICH E9(R1); hypothetical estimand; principal stratum estimand

  • Mouna Akacha, Novartis Pharma AG, Basel, Switzerland
  • Björn Bornkamp, Novartis Pharma AG, Basel, Switzerland
  • Alex Ocampo, Novartis Pharma AG, Basel, Switzerland
  • Jiawei Wei, Novartis Institutes for Biomedical Research Co., Shanghai, China

This half-day short course introduces how causal inference approaches are relevant and used in the implementation of estimands framework in drug development. It includes 4 lectures:

Lecture 1 - Introduction to Estimands and Causal Inference:

  1. Overview of the estimand framework and key points in ICH E9(R1)
  2. Introduction to causal inference, including potential outcomes, causal effects and common assumptions;

Lecture 2 - Estimation Methods of Causal Effects Targeting at Hypothetical Estimands:

  1. Introduction to common estimation methods, e.g., g-computation, IPW (Inverse probability weighting)
  2. RCT examples illustrated using R code;

Lecture 3 - Principal Stratum:

  1. Introduction to principal stratum estimand
  2. Estimation strategies
  3. Case studies in RCTs;

Lecture 4 - Conditional and Marginal Treatment Effects:

  1. Introduction to conditional and marginal treatment effects
  2. Appropriate estimators for conditional and marginal estimands
  3. RCT examples illustrated using R code.

Go fastR: High Performance Computing with R

Hybrid?: No.
Keywords: statistical computing, R; HPC; clustermq; batchtools; Stan; Bayesian; clinical trial simulations

  • Michael Mayer, Posit PBC, Boston, USA
  • Lukas Widmer, Novartis Pharma AG, Basel, Switzerland

This course will help participants to optimize their R code as well as parallelizing and debugging it on their own machines as well as high-performance computing environments. Example use cases include commonly performed activities for trial design, bootstrapping, cross-validation and related workloads. The following topics will be covered:

Part I: Identifying bottlenecks in your R code, debugging, and optimizing

  • Debugging R code & checking correctness
  • Profiling R code to identify bottlenecks
  • Optimizing bottlenecks locally: packages, vectorizing, logical indexing

Part II: R parallelization on high performance computing environments (HPCE)

  • Amdahl’s law and limits of achievable speed up
  • Parallelizing work onto compute clusters via clusterMQ and batchtools
  • Consistently loading packages, .libPaths() and options() on R workers
  • Uncorrelated random number generation for parallel R code
  • Debugging R code in batchtools and clusterMQ jobs

Part III: Case studies and code examples

  • Bootstrapping
  • Cross-validation
  • Trial simulations under replication
  • Within-chain parallelization with several chains in Stan
  • Bring your own problem: start to speed-up your own code with the help of the instructors.

Target Trial Emulation for Causal Inference from Real-World Data

Hybrid?: Yes.
Keywords: observational data, avoding self-inflicted biases, comparing treatment strategies

  • Vanessa Didelez, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
  • Maria Geers, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany

Target trial emulation (TTE) is a general principle to organize and structure the analysis of observational data, such as electronic health records, claims or registry data, so as to minimize common but avoidable sources of bias, e.g. immortal-time bias. Moreover, formulating a target trial is helpful to elicit practically meaningful causal research questions (aka “estimands”) with a clear interpretation. The workshop will explain the principle of TTE using examples from cancer screening, drug safety as well as nutritional epidemiology. For instance, we will illustrate how to emulate a target trial on screening colonoscopy, how this avoids design-related and other biases, while showing how results are badly affected if a naive study design is chosen that suffers from these biases. A brief overview of some relevant statistical methods will be given, such as the clone-censor-weight approach or the parametric g-formula. However, as will become clear, TTE is a fundamental principle that can be combined with various causal inference methods.

Organization of the workshop:
There will be theoretical parts as well as worked examples, with hands-on tasks for the participants.

Learning outcomes:
Participants will (i) be able to recognise avoidable sources of bias in naïve studies using observational data; (ii) become aware of basic techniques to avoid these issues; (iii) acquire a basic understanding of TTE that will facilitate studying the more advanced literature.

Improving Precision and Power in Randomized Trials by Leveraging Baseline Variables

Hybrid?: Yes.
Keywords: covariate adjustment; causal inference; standardization; treatment policy; robustness; group sequential designs

  • Kelly Van Lancker, Ghent University, Belgium
  • Michael Rosenblum, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.
  • Josh Betz, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.

In May 2021, the U.S. Food and Drug Administration (FDA) released a revised draft guidance for industry on “Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products”. Covariate adjustment is a statistical analysis method for improving precision in clinical trials by adjusting for pre-specified, prognostic baseline variables (e.g., age, BMI, comorbidities). The resulting sample size reductions can lead to substantial cost savings, and also more ethical trials since they avoid exposing more participants than necessary to experimental treatments. Though covariate adjustment is recommended by the FDA and the European Medicines Agency, many trials do not fully exploit the available information in baseline variables.

In Part 1, we explain what covariate adjustment is, how it works, when it may be useful, and how to implement it (in a preplanned way that is robust to model misspecification) for a variety of scenarios.

In Part 2, we present a new method that enables us to easily combine covariate adjustment with group sequential designs. This approach can lead to faster trials, without sacrificing validity or power, even when the experimental treatment is ineffective.

In Part 3, we show the impact of covariate adjustment using completed trial datasets in multiple disease areas. We provide step-by-step, clear documentation of how to apply the software in each setting. Participants will have the time to apply software tools on the different datasets.

Model and Algorithm Evaluation in Supervised Machine Learning

Hybrid?: Yes.
Keywords: prediction; performance; validation; comparison; benchmark

  • Max Westphal, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
  • Rieke Alpers, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany

The statistical evaluation of developed models and algorithms is an essential part of applied machine learning and predictive modelling. This half-day course is suitable as a concise introduction or refresher for this important topic. It is divided into three parts with sufficient time for participant questions and breaks in between.

Initially, we will repeat essential machine learning basics and cover core concepts of model evaluation. We will mainly consider classification tasks and the most relevant assessment criteria (discrimination, calibration) but also summarize adaptations for regression and survival problems. In the main part, we discuss common pitfalls (leakage, multiplicity, ….) in model evaluation and appropriate best practices to avoid and/or rectify them. Finally, we touch upon some advanced topics and cover important practical aspects (software, reporting, reproducibility) that are required for a successful evaluation study.

The course contents are illustrated by means of real-world data examples, including R code to showcase how the numerical results were obtained. There are no explicit coding sessions in this short course, so a laptop is not necessarily required. The course materials will be made available so that participants have the opportunity to individually reproduce the numerical examples after the course.


  • Initial practical experience in applied machine learning
  • Basic knowledge of descriptive and inferential statistics