I have data for hundreds of individuals over several (often tens) of years. My dependent variable is a binary event that may or may not have happened for each individual in some given year. The event can only happen once per individual. Independent variables include individual characteristics that do not vary over time (e.g., gender) and measures for each individual that vary over time (e.g., output of that person in year t).
Minimum viable example of my data for two individuals with far less observations per individual and considerably less independent variables than I truly have:
My aim is to test whether each independent variable explain the occurence of the event. I would also like to keep the possibility to assess whether the fixed characteristics like gender could explain the event even when the time-varying measures are similar between individuals.
At this point, I am not sure which model to use. I was considering logistic regression, but the observations are probably not independent as there are several measurements across individuals. Other options that have come across thus far have been survival models and mixed-effects models, but I am not familiar with them, so I cannot properly judge whether these models would suit my problem better.
What model(s) should I consider given my data and objectives? I am happy to provide any further details if necessary.
Thanks in advance!