# How to model time to event / survival analysis when event outcomes are not binary

My data set looks like this

ID   Weak_year  Strong_year  Covariate1 Covariate2 ...
1    1966       NA           0.35       0.29
1    NA         1987         0.97       1.20
2    NA         1970         0.76       0.90
3    1975       NA           1.4        3.2
4    ...
...


So each object can produce either a weak reaction, a strong reaction, or a weak and then strong reaction. Weak_year is the year a weak reaction happens, strong_year is the year a strong reaction happens. The covariates are independent variables; their values are the values for the year that the reaction events happened.

The start of the study is 1965 and the censoring time is 1990. Some observations are right-censored.

I'm familiar with survival analysis of binary outcomes (event or non-event), but I'm not sure how to model the time to event when there are three outcome levels of different strengths like these. What are some options to think about? It would be especially useful if these options can be implemented in R (that's what I'm using).

Multistate models are not difficult, at least not if you employ a number of assumptions, such as the transitions being Markov. There is a huge amount of literature on the topic, and I am sure you can find a lot online, including books on how to use these in R. To implement such a thing in R is not necessarily difficult, but it requires a bit of fiddling and putting the data in a nice form. For simple ones, you can actually use the survival package (there is a vignette on the topic). Other two packages specialized for multistate models are mstate and msm. Both come with tutorials that you can find online.