I am trying to design a theoretical study in which one group of patients uses two wearable devices (i.e., every participant uses both devices) that gather information about their vitals, which is then fed into an algorithm that predicts their risk of heart failure. I am thinking of using a time-to-event analysis, in which the time to event is the time between the point at which the algorithm predicts a heart failure event, and then the point at which the patient is hospitalized for heart failure. (e.g., the algorithm may predict a heart failure event on Day 1, but the patient won't be hospitalized until 4 days later. The longer this time is, the better, since I am trying to identify the device with the earliest prediction.) I want to compare the performance of the two devices (and their respective algorithms) in predicting heart failure events, meaning that the data will be paired, since each participant is providing data for both devices. Since I already know the median prediction time for one of the devices is 6.5 days, based on a prior study which inspired the current design (Free access: https://www.ahajournals.org/doi/10.1161/CIRCHEARTFAILURE.119.006513), I want to use a non-inferiority approach to demonstrate that the other device is at least as effective as the former.
Since this is all a theoretical study, the most important thing for me at the moment is to be able to identify the sample size that would lead to results with high power. (I also don't know if stratifying my population would affect things, but I was also hoping to do that.) My problem at the moment is that the study linked above doesn't have any information on hazard ratios, so I'm not sure how to proceed with median survival time as my reference.
What technique would be best for something like this? As you probably can tell, I'm not a statistician, so if software tools exist that can do something like this, I would prefer to use those.
Sorry for the huge question, and thank you in advance for your help!