Speaker: Kenneth Man 

Speaker Bio: Dr. Man is a pharmacoepidemiologist and medical statistician specialising in using electronic health records and big data to address critical questions about medication safety and effectiveness. His work incorporates advanced methodologies, such as propensity scores, self-controlled designs, target trial emulation, and multinational data harmonisation to generate real world evidence that informs healthcare practices worldwide. His research primarily focuses on psychotropic medications, including their use in children, adolescents, and during pregnancy. He has authored over 160 studies that were featured in prestigious journals such as Nature Medicine, the BMJ, and JAMA, with my work also referenced by regulatory bodies and clinical guidelines worldwide. He has received several prestigious awards, including the Kramer-Pollnow Prize for excellence in ADHD research and funding from the CW Maplethorpe Fellowship, the European Commission Horizon 2020 Framework (TIMESPAN) as well as the UK National Institute of Health Research. 

Brief description:  
Observational data can be misleading when emulating randomized trials, especially when treatment assignment occurs after cohort entry. The clone-censor-weight (CCW) method offers a principled solution within the target trial emulation framework by aligning eligibility, treatment assignment, and follow-up time. This talk will explain how cloning individuals into multiple treatment strategies, artificially censoring deviations, and applying inverse probability weights can mitigate immortal time bias and emulate the intention-to-treat principle. We will use practical examples to illustrate implementation steps, key assumptions, and pitfalls. This session is ideal for students and early-career researchers interested in real-world evidence and advanced epidemiologic methods. 

Learning objectives / key takeaways 

1. Understand the rationale and motivation for using target trial emulation in pharmacoepidemiology. 

2. Learn the concept of "time zero," eligibility, and treatment assignment in observational studies. 

3. Understand the problem of immortal time bias and how the clone-censor-weight (CCW) approach addresses it. 

4. Describe how cloning, artificial censoring, and inverse probability weighting work together in the CCW method.