What You'll Learn in This Workshop

Gain a deep understanding of key concepts and practical applications through a combination of expert-led live sessions, interactive case studies, and hands-on exercises. Most sessions are planned to be hosted live, providing opportunities to engage directly with instructors, ask questions in real time, and participate in peer discussions. While the overall structure is designed to help you build both theoretical understanding and practical skills, please note that the program content and format are still subject to refinement. Our goal is to equip you with the confidence and tools to apply advanced propensity score methods effectively in your own research or professional work.

    1. Understanding Administrative Health Data

    2. Study Design in Observational Research

    3. Challenges in Observational Studies: Bias and Analysis

    1. Understanding Propensity Scores: A Beginner's Guide

    2. Ensuring Balance: Assumptions, Estimands, and Best Practices

    3. Leveraging Propensity Scores for Causal Inference

    4. Best Practices for Applying Propensity Score Methods

    5. Disease Risk Scores: An Alternative to Propensity Scores?

    6. Propensity Scores in Pharmacoepidemiology: Challenges and Innovations

    7. Further Readings

    1. Case Study Overview: High-Potency Statins and Kidney Injury

    2. Analyzing High-Potency Statins and Acute Kidney Injury

    3. Step-by-Step Guide: Setting Up the Practical Exercise

    4. Journal Club Discussion: Kurth et al. (2006)

    1. Principles of Confounder Selection in Causal Inference

    2. Best Practices in Confounder Selection

    3. High-Dimensional Propensity Score for Confounder Selection

    4. Machine Learning for Propensity Scores

    5. Double Robust Methods

    6. Bayesian Propensity Scores

    7. Further Readings

    1. Application: Standardization & Matching

    2. Application: Weighting

    3. Advanced application: hdPS

    4. Advanced application: hdPS and double-robust methods

    1. Open Questions and Next Steps

About this course

  • €1.500,00
  • 26 lessons

Learning Objectives

Theory Meets Practice: Essential Skills for Propensity Score Methods

  • Understanding and Addressing Confounding Bias

    Learn statistical techniques to adjust for confounding in pharmacoepidemiology and real-world evidence generation.

  • Fundamentals of Propensity Scores

    Explore propensity scores, their role in causal inference, and best practices for estimation and application.

  • Propensity Score Methods for High-Dimensional Data

    Apply propensity score models in high-dimensional datasets by managing feature complexity and ensuring covariate balance.

  • Enhancing Causal Inference with Robust Methods

    Improve reliability using double-robust techniques and targeted learning approaches.

  • Machine Learning for Causal Inference

    Discover how machine learning improves covariate balance and reduces bias.

  • Hands-on Practicals

    Apply propensity score techniques in R and SAS with real-world case studies for reproducible workflows.

Designed for Professionals Like You

This workshop is designed for professionals seeking to refine their expertise in confounding control and causal inference in pharmacoepidemiology. Whether you're working in research, industry, or regulatory decision-making, this workshop will equip you with cutting-edge methods to improve causal estimation in observational studies.

  • Pharmacoepidemiologists – Enhance your ability to control confounding in real-world data analyses, improving causal inference for drug safety and effectiveness.

  • Statisticians & Data Scientists – Gain advanced skills in propensity score techniques to strengthen the validity of observational study results.

  • Clinical & Pharmaceutical Industry Professionals – Improve your ability to evaluate and apply propensity score methods in health economics, outcomes research (HEOR), and regulatory submissions.

  • Academic Researchers & Students – Learn modern techniques in confounding control to improve the rigor of your research and publications.

  • Regulatory Scientists & Policy Makers – Develop a deeper understanding of how propensity scores influence real-world evidence and policy decisions.

Instructor(s)

Robert Platt

Professor

Robert Platt, PhD, is a recognized expert in causal inference and pharmacoepidemiology, with extensive experience developing statistical methods for observational studies and clinical trials. He has authored 400+ peer-reviewed articles and supervised over 50 research trainees in epidemiology, biostatistics, and statistics. Prof. Platt has held leadership roles in professional societies, including serving as President of the Statistical Society of Canada and the Society for Pediatric and Perinatal Epidemiologic Research. He is a Fellow of the American Statistical Association and the International Society for Pharmacoepidemiology. In addition to his research and mentorship, he plays a key role in academic publishing, serving as Editor-in-Chief of Statistics in Medicine and as an editor for the American Journal of Epidemiology. His work continues to shape the fields of epidemiology and biostatistics globally.

Thomas Debray

Founder, Smart Data Analysis and Statistics

Dr. Thomas Debray is a leading expert in precision medicine and real-world evidence, with over a decade of experience in biostatistics. He has held research positions at Utrecht University, University College London, and the University of Oxford and is a guest scientist at University Medical Center Göttingen. As the founder of SDAS, he collaborates with pharma and biotech companies to advance clinical trial design, predictive modeling, and evidence-based decision-making.

Learn by Doing – Live and Interactive Sessions

Join a community of experts in this immersive, hands-on workshop and take your skills to the next level. Don't miss this opportunity to refine your expertise in confounding control and modern causal inference!

Reserve Your Spot