Researcher: Kgoale Tshiamo Mahlako
Supervisor: Dr A Whata
Survival analysis is a vital field in statistics, offering valuable insights into various domains, from healthcare to epidemiology. It allows us to understand the factors that influence time-to-event outcomes. The analysis of survival data comes with its own set of challenges, especially when it involves censoring.
Censoring occurs when we don’t have complete information about the event times, making it difficult to draw causal inferences. The application of pseudo-observations, which is a crucial tool for enhancing causal inference, will be the subject of our particular attention. We apply the G-formula and IPTW, methods from causal inference, on these pseudo-observations. And estimate the ATE for a completely observed outcome and censored data.
