Modeling hazard rates with mutliple time scales: An application study

There are situations when we need to model multiple time scales in survival analysis. A usual approach would involve Ktting Cox or Poisson models to a time-split dataset. However, this leads to large datasets and can be computationally intensive when model Ktting, especially if interest lies in displaying how the estimated hazard rate or survival changes along multiple time scales continuously. Flexible parametric survival models on the log-hazard scale are an alternative method when modeling data with multiple time scales. This can be achieved by using the Stata package stmt, where one of the time scales is chosen to be a primary time scale, and the other time scale(s) is(are) speciKed by using the offset option. Through a case study, I will demonstrate this method and provide examples of graphical representations.