To my knowledge those would be considered left censored and should be excluded from the analysis.

Or is there another method to analyze "negative" time to event values?

Given the first sentence of your question I would exclude groups B, C because they cannot tell you anything about time from full vacation to COVID infection time interval which appears to be your research question. Best wishes David Booth

David, if the plan is to use time-to-event (TTE) analysis, why would you exclude folks who have not experienced the event by the end of the study? As the following excerpt notes, it is incredibly common--more common that not, I think--to have such censoring in TTE analysis.

"One of the challenges specific to survival analysis is that only some individuals will have experienced the event by the end of the study, and therefore survival times will be unknown for a subset of the study group. This phenomenon is called censoring and may arise in the following ways: the study participant has not yet experienced the relevant outcome, such as relapse or death, by the close of the study; the study participant is lost to follow-up during the study period; or, the study participant experiences a different event that makes further follow-up impossible. Such censored interval times underestimate the true but unknown time to event. For most analytic approaches, censoring is assumed to be random or non-informative."

Source: https://www.publichealth.columbia.edu/research/population-health-methods/time-event-data-analysis

Khaled Tarek, the link for the Hosmer, Lemeshow & May textbook examples at the bottom of that page has not been updated with the new address for UCLA pages. Here is the correct address:

• https://stats.oarc.ucla.edu/other/examples/asa2/

HTH.

@Bruce Weaver thank you so much i will check it out

Yes, the coding you proposed for data A and B is correct. For data C, you can consider the event as occurring before the study period, so the event would be coded as "TRUE". However, the time to event would be a negative value, representing the time between infection and vaccination. In this case, you could use the negative value as an indicator that the event occurred before the study period. This negative value would then indicate that the infection happened before the study period, which makes sense given that these participants got infected before vaccination.