**Abstract**

Statistics and statisticians play a vital role not just in the analysis and interpretation of data generated in clinical trials but also in the design and conduct of clinical trials. For a fixed design, i.e. a design without interim looks, the statistician at a minimum calculates the sample size based on prior information or clinical assumptions, decides in discussion in the clinical team the primary endpoint(s) that will provide evidence towards the safety and efficacy of a drug, biologic, vaccine or a medical device and the statistical analysis to be conducted at the end of the trial. Since around 25 years or so, adaptive designs have become more and more popular not just in early phase discovery trials but also in late phase confirmatory trials. These adaptive designs gives clinical trialists the opportunity to rectify the design following interim looks in case some of the prior assumptions during trial design seem unrealistic in light of the interim data. Rectification of the design in turn minimizes the probability of failure of the trial to show the desired evidence even when the product works. In a nutshell, adaptive designs are designs where following one or more interim looks, pre-specified, data-driven changes are possible for the future course of the trial.

Some common adaptive designs can be constructed and optimized using asymptotic results, however, in most cases simulations are required for establishing the optimal design parameters and operating characteristics of the design. In any case, simulation help towards more rigorous planning. One special class of designs that are becoming very popular in oncology, rare diseases, pediatric indications and even in the vaccine space are Bayesian designs using Bayesian statistics. These are exclusively simulation-guided designs.

In this course we will learn the basics of such simulated-guided adaptive clinical trial designs both in the traditional frequentist as well as in the Bayesian framework via case studies (early and late phase clinical trials) and using some programs and software packages in R.

List of topics (tentative, not in order)

• Basics of randomised clinical trials (RCT), clinical trial design and statistical planning

• Background on regulatory requirements

• Introduction to group sequential and adaptive designs

• Fundamental difference between traditional Frequentist and the Bayesian framework

• Introduction to Bayesian designs

• Case study: Early Phase Dose-escalation design with practical session using R

• Case study: Late phase adaptive group sequential design with practical session using R

• Case study: Bayesian design in a rare disease setting incorporating historical data with

practical session using R

• Case study: Bayesian Vaccine trial design with practical session using R

Prerequisites

• Some background on randomised clinical trials (RCTs), statistical hypothesis testing and

estimation and Bayesian statistics (optional)

• Requirements for the practical sessions:

1. Hands on experience working with R, for examples, writing functions, running for

loops, use of apply and lapply functions, using R’s basic plot method.

2. Knowledge of using parallel computing and using ggplot is desirable but not required.

3. Installation of R„ version 4 or above and Rstudio, version 2023 or higher.

4. R-packages: tidyverse, parallel, gsDesign, rpact, AGSDest, PhIdesign, BOIN,

learnBayes, RBesT, mcmc, ggplot2, survival, mvtnorm, rstan, rstanarm, hdbayes

(not available for windows). Some packages may not be available in CRAN, you may have to install it from github or other repositories. For example, the package PhIDesign can be installed using the line of code in R: remotes::install_github("IDDI-BE/PhIdesign",build_vignettes=TRUE) Please make sure to use also install the dependencies by adding the dep=TRUE option while calling install.packages or from the drop-down menu. Make sure that these packages load okay in your R session.

5. Some useful pre-reads: click on the links to the html pages below

– LearnBayes Binomial Vignette

– LearnBayes MCMC Vignette

– RBesT Intro Vignette

– Intro to ggplots

**Abstract**

To discover new drugs is to seek and prove causality. Causal inference combines model and data to identify causations from correlations and is indispensable for intervention, "what if" questions, and understanding. The first part of the course will introduce the basic concepts of causal modelling with directed acyclic graphs (DAGs), the identification and estimation of causal effects, and causal model selection. The second part of the course will focus on learning large-scale causal Bayesian networks from population genomics data and controlled perturbation experiments, and how such networks are used to understand mechanisms, identify candidate targets, and discover drug repurposing opportunities.

List of planned topics:

- What is causal inference

- Causal inference with DAGs

- Common structural motifs in DAGs and how to interpret them

- Causal effect identification using the backdoor and frontdoor criteria

- Causal effect estimation from data

- Statistical model selection

- Mendelian randomization

- Bayesian network learning

- Population genomics data resources

- Controlled perturbation experiments resources

- Drug repurposing methods

Lecture notes, example notebooks, and requirements for the practical sessions will be made available at https://github.com/tmichoel/causal-inference-short-course closer to the course date.

**Ana Jacinta Soares, CMAT, University of Minho**

**20 June, 10:00 – 12:00 and 14:00 – 16:00; 21 June, 10:00 – 12:00**

**Abstract**

**The kinetic theory is a branch of statistical mechanics that provides a detailed description of systems of a large number of particles, agents, or other entities. One of its main tasks is to deduce the macroscopic properties of the system from the kinetic dynamics and derive governing equations for these macroscopic properties in the hydrodynamic limits. Therefore, kinetic theory offers a convenient approach to several problems where different scales play a significant role in dynamics. **

Particularly in a biological context, when considering a large system of multicellular populations, the kinetic theory of active particles provides a model where the properties expressed by the cells are described by a microscopic activity variable, and cell proliferation and destruction are incorporated into the description.

From a mathematical point of view, the model is formulated in terms of partial-integro-differential equations for active cellular populations and ordinary differential equations for global populations. Mathematical tools of kinetic theory combined with dynamical systems techniques allow us to address many interesting problems in the biological context.

In this course, we will explore how the kinetic theory of active particles can be used to develop a model for the interactions of the immune system and how we can derive a macroscopic model for the evolution of global quantities from such a detailed model. Then, we will apply these tools to model immune system disorders and cancer diseases, incorporating cell proliferation, destruction, and other effects. These models can also incorporate treatment. We will specialize some models and present some numerical simulations to illustrate the modeling capabilities.

List of planned topics :

– modelling of autoimmune diseases

– modelling of tumor growth

– immunotherapies and control

– chronicity and recurrent behaviours

– formation of patterns

Helpful background

Some background in the theory of differential equations and dynamical systems will be useful.

Additionally, elementary skills in numerical simulations with symbolic software may be beneficial.