In a contest for solving a problem, a person submits his/her answer for the problem at any time during the contest. The contest system provides the person with feedback on the submitted answer. The person can resubmit a modified answer based on the feedback at any time during the contest. This process is repeated until the end of the contest.
I have a dataset for the contest as follows:
Table 1
person feedback submission_time
A 50 20
A 80 50
A 90 60
B 10 35
B 11 80
...
My interest is to estimate how quickly people submitted their answers based on feedback. In other words, how does feedback at the previous time affect the probability of submitting a revised answer? For this purpose, I first considered a survival model and modified the above model as follows:
Table 2
person feedback time0 time1 submission
A . 0 20 1
A 50 20 50 1
A 80 50 60 1
A 90 60 0
...
As seen in the above table, every submission is present except for the final submission. In this case, does a survival model such as the Cox proportional hazard model provide the proper estimates for my research objectivw? Secondly, are there more suitable statistical models to address my objective than a survival model?
