I am planning to do time-to-event analysis. The event is mortality. The outcome is obesity w/wo metabolic risk factors. N=16,000. My first idea was to use traditional survival models such as Cox PH. However, digging into literature regarding both the health topic and time-to-event analysis, I came across decision trees, and specifically TSSA. The way I understand TSSA is that it will be possible to generate subgroups based on the data, which have differing mortality rates. I.e. decision trees. Hence, it will be possible to examine which of the risk factor combination carries the most risk of mortality, and allow for more complex and data-driven interactions, non-linearity etc. On the other hand, I cannot find many studies using this method and I also fear overfitting.
- Does anyone have any experience with decision tree vs regression survival analysis?
- Which package in R could be recommended?