I work in a lab where group members sometimes individually carry out an experiment of the following form:
- pairs are bred at some definite time
- the pairs are grouped by in experimental treatments (i.e. 25 pairs in condition A, 25 pairs in B)
- the time to a certain event occuring is measured
Usually we would analyse this data in the lazy/standard way - i.e. we would use linear models to look for differences between group means and then ANOVA to test if the difference in group means are significant between treatments.
I have recently realised that as our data is interval-censored (i.e. we don't really record event time-points, but windows of time in which the event occured), and have begun using interval-censored survival models.
What I can't give is a coherent explanation of why the former approach is a poor one. What is actually better about survival analyses than linear models for analysing survival data?