Multiple endpoints in survival analysis of individual careers I analyze labour market activities. I measure JD: job duration (time between beginning and ending of employment) and PD: professional duration (the time spent in a specific vocation). My sample is episode based (not individual based), thats important for PD: I measure PD as two subsequent job episodes in the same vocation. PD therefore is greater than or equal to JD by definition. Furthermore I have more data points for JD (every single employment gives a data point) than for PD (for every employee two subsequent employments in the same vocation are merged to one data point).
I am interested in the difference between JD and PD and I want to compare this difference for multiple groups.
Testing JD and separately PD isn't desirable. Suppose group A has a short JD and a slightly longer PD. Group B could then have a long JD but a PD that's only slightly longer than B's JD. What test can be performed to check for a difference of the difference between JD and PD?
Does it makes sense to test for a difference in proportions of JD to PD in groups?
Non-sequitor: what does the rho do in R's survdiff-test and how should I choose rho?
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My approach still seems unclear. Here's what I do exactly:
I use a database storing people's id (ID), begin data of an occupation (BEGIN), end date of an occupation (END) and type of occupation (TYPE).


*

*To gather JD I take BEGIN and END and build different groups using TYPE.

*To receive PD I do the following: For every ID if 2 subsequent employments (e1, e2) have the same TYPE, I merge them to one episode (with BEGIN(e1) and END(e2)). I repeat this step until no more merges are possible (e.g. if there are 3 subsequent employments (e1, e2, e3) of the same TYPE, I end up with one episode (with BEGIN(e1) and END(e3))


Finally, what I try to find out is, if the difference between JD and PD follows a similar pattern for different types or not.
 A: If you want independent time-to-event models, you could calculate a "delta" ID = PD-JD or an interim duration (unemployment possibly, or time in subsequent jobs of similar function). Time-to-new-vocation is a meaningful outcome on its own. In such models, you could adjust for the JD by having it as a covariate/stratum variable in a Cox proportional hazards model. This would allow you to compare people of different job markets with the same duration of employment. So people in the medical field with 5 years experience versus people in the architecture field with 5 years experience would be compared in their rate of re-employment.
Looking at the fraction of JD to PD is possibly a meaningful analysis, though intuitively I don't believe there's any reason to assume that the shorter the time of employment, the shorter the time until finding another capacity.
I think you should also consider the multiple endpoints of people who float around in several jobs in the same capacity, i.e. people in a desirable market with commitment issues or contractors. It doesn't seem like you've captured that information according to your problem description.
