My application is not a traditional survival analysis scenario. However, I believe survival analysis methods, e.g., Cox regression, can be a possible solution. In particular, my dataset contains two kinds of data:
- Left-censored: For example, I know a patient has been dead at time t8. But the exact time point that the patient died is unknown. Also, in the nature of the problem, it is impossible to know.
- Right-censored: Similar in the traditional case. For example, I know a patient was alive from t0-t6. But it is unknown when the patient died after t6.
In both case, the exact "death point" is unknown. My data only contains these two cases.
I plan to use widely used survival analysis packages (e.g. lifelines) to solve this problem:
- Left-censored: For the example above, I will label it as "duration: t8, event:1". (note that t8 is NOT the exact time that the patient dies)
- Right-censored: For the example above, I will label it as "duration: t6, event 0".
- Does using Cox regression make any sense?
- How does these two types of data impact the final model? Say, under-estimate/over-estimate the baseline hazard?
- Are these any other models can better handle this case, instead of Cox regression?