Estimating Treatment Effects Without Randomization: Propensity Scores in Real-World Oncology Studies
Randomized clinical trials remain the gold standard for causal inference, but many clinically important questions in oncology must be addressed using real-world observational data. This seminar will focus on the use of propensity score methods to estimate treatment effects in non-randomized settings, with an emphasis on practical implementation, assumptions, and limitations. Using two real-world oncology case studies, I will illustrate how different propensity score approaches (matching and inverse probability of treatment weighting) align with different causal estimands and clinical questions. The talk will highlight common pitfalls, the importance of thoughtful covariate selection and balance diagnostics, and how observational analyses can inform, but not replace, randomized trials.
In-person attendance is encouraged. To join us virtually, please use this link: https://bit.ly/APortuguese