Health economics: choosing the right models to analyze of survival data
What is it about?
"It's tough to make predictions, especially about the future" - Yogi Berra Sill, when evaluating interventions that impact survival, we need to estimate long-term benefits based on current data. This involves analyzing trial data up to the maximum follow-up period and using parametric models to make projections. Typically, these models are compared visually to the Kaplan-Meier survival estimate, and likelihood-based information criteria are used to assess the models. However, this work suggests a different approach, focusing on minimizing the difference between the parametric estimators and the Kaplan-Meier estimate. This method aims to ensure that the extrapolated model accurately represents the data. The process is demonstrated using both simulated and real-world data, including a scenario where no suitable model was found, to show how this approach can assist in model selection.
Why is it important?
Eliminates subjective decision making with objective and measurable framework.