Exciting New Courses Offered by SIGMA Consortium Members

14 March 2025

The SIGMA Consortium is excited to share the upcoming courses from our members. Tailored for professionals in pharmacoepidemiology/real-world evidence (RWE), these courses are designed to provide advanced knowledge and practical skills in various aspects of drug safety and effectiveness research. Participants will have the opportunity to engage with leading experts in the field, explore real-world applications of pharmaco-epidemiological methods, and enhance their understanding through hands-on learning experiences.

1. Towards Regulatory-Grade Causal Inference from Observational Data

This 2-day in-person course covers the target trial framework for assessing comparative effectiveness and safety using healthcare databases. It includes practical use cases and addresses participants’ questions about their own observational analyses.

2. Validate Study Variables to Reduce Misclassification Bias: Recent Tools and Research Needs

This free course addresses the challenges of validating study variables and offers modern tools for reducing misclassification bias. It aims to improve validation practices and integrate methodological techniques with real-world applications.

  • Date: 27 March 2025
  • Location: Florence, Italy
  • Host: ARS Toscana
  • Format: In-Person or Online
  • Additional information and registration:

3. Bordeaux PharmacoEpi Festival

The Bordeaux PharmacoEpi Festival, established in 2010, is an annual event where experts in pharmacoepidemiology gather to discuss the latest developments and public health issues. The unique master class format ensures that attendees remain up-to-date with significant developments in pharmacoepidemiology.

4. 10th Pharmacoepidemiology Summer School: (Randomized) Trials and (Systematic) Errors: A Pharmacoepidemiology Bootcamp

This intensive course is tailored for PhD students, postdocs, clinicians, and professionals seeking to enhance their expertise in pharmacoepidemiology. It will cover randomized trials, vaccine safety, self-controlled study designs, perinatal pharmacoepidemiology, and the use of artificial intelligence in the field. The goal is to provide a thorough understanding of these topics, building on foundational knowledge in epidemiology and biostatistics.

5. Pharmacoepidemiology and Drug Safety Summer School

This summer school comprehensive programme covers essential topics in pharmacoepidemiology, including study design, drug safety, and risk management. Participants will learn about methodological issues, confounding biases, and the use of pharmacoepidemiological databases. The course aims to enhance stakeholder interaction and prepare attendees for innovative drug therapies and their safety evaluation.

6. Molecular Pharmacoepidemiology

This course is designed for researchers, healthcare professionals, and students seeking to understand how molecular and genetic data can be used to predict drug safety and efficacy and optimize personalized medicine approaches. Participants will explore how omics technologies and artificial intelligence (AI) methods are applied in pharmacoepidemiology to assess and predict medication responses.

7. Introduction to Causal Inference and Causal Data Science

In this course, participants will learn about the latest methods for answering causal research questions using non-experimental data, and gain hands-on experience applying these methods to data using R. The course will introduce research to the two main methodological frameworks for causal inference: (1) The potential outcomes framework and (2) Directed Acyclic Graphs (DAGs) together with structural causal models. The latter part of the course will focus on using these basic tools and techniques in more advanced and realistic settings. Participants will learn about the method of target trial emulation to guide the design and analysis of causal inference projects; how causal graphs can be estimated from data using structure learning algorithms; how causal inference principles can guide and interact with prediction modeling techniques from data science; and other advanced topics in causal modeling, such as longitudinal and quasi-experimental settings.

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