I understand how survival methods can be used to determine the probability of survival given a dataset of 'time-to-events' with almost all examples considering cases of Alive/Dead e.g., cancer. However, how can I factor in cases of multiple remission and relapse events per person in a disease that will not take a life?

For example. Remission is defined by the absence for more than 90 days from medication or disease in a patient record. A relapse is returning to a similar medical/drug state any time after a single day beyond the 90-day disease/remit cut-off. To be considered as having ongoing treatment, there will be a continuous record of either drug prescription or disease code for less than 90 days at intervals (more than 90 days and we assume that the patient is in remission). e.g., visiting a doctor (or repeat prescription using the NHS model) at least once every three months.

Using these definitions, I can take an individual's medical history and table the number of days until time-of-event of diseased and remission. A good drug will mean remission was longer than diseased, or at least diseased is kept as short as possible even if there is then only a very short remission time. For example, Bob gets disease X at t=0 (and a drug prescription) and I start counting the number of days until there has been a 90-day absence of either, at which point I start counting the number of days as remission until the same drug or same disease appears and then I start counting again but for a diseased state.

patid days event

1 200 D (diseased)

1 450 R (remit)

1 340 D

1 500 R

2 ... D

2 ... R

I am using R and providing this data into the Cox regression function as though patid 1 (the first patient) is actually 4 people! Similar to how 4 people would be alive/dead in a cancer model.

I have coded all the logic to break down a group of individual's records into stages of diseased or remission. However, is it correct in a cox model (in R) to provide this information as is?