We're studying operators in a particular transportation industry in Europe. The data are being collected as part of a larger project, and so far we've only analyzed in a very specific manner to satisfy government regulation. There's a lot more we could do with the data, so I'm exploring different avenues of doing so.
We also have data that aren't repeated measures like BMI, sex, and age.
The basic setup of data collection is this:
We have data on circadian phase, reaction time, sleep habits, fatigue, etc. In total, we're repeating these measurements for each participant at 16 different points in time. At the highest level, we're collecting data for each operator on routes A and B. Within each route, we collected data on the operator's outbound trip as well as their inbound trip. For each inbound and outbound trip, we collected data at 4 different times.
My question: If I was interested using data across the entire study period to model fatigue, is there an elegant way of doing that? I'm not particularly interested in finding differences between routes A/B or inbound/outbound, just modeling how variables like circadian phase and prior sleep predict fatigue.
