Hello there! I have hit a bit of a wall with my time-stratified case-crossover study for my dissertation. I have looked back at the previous discussions - as well as Tobias' paper from 2014 (Armstrong BG, Gasparrini A, Tobias A. Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis. BMC Med Res Methodol. 2014;14:122. Published 2014 Nov 24. doi:10.1186/1471-2288-14-122) and I still have not really found a solution. My data is individual and not aggregate, so it seems like conditional logistic regression is the appropriate type of regression to use. However, I need help restructuring the database for the clogit in STATA. I can't figure out how to weigh the data and restructure so I can run the clogit appropriately (and with validity). Thank you so much in advance!!

case: human West Nile Virus case status (case = 1)
control: self-matching 1 case to 3 self-controls (control = 0)
cases - five separate weeks of 7 different types of weather data, calculated from the date of West Nile Virus Diagnosis
controls - three separate weeks of 7 different types of weather data.

The P_1WK is precipitation the week prior to the diagnosis. The P_2WK is the precipitation the week that is two weeks prior to diagnosis, etc.up to P_5WK
P_1WK_FWD are the control weeks that are one week forward from the control date (there are three per case, which are time-stratified before and after the exposure hazard period)
TAVG is the average temperature, etc.

Here is a sample of my data (I have removed the diagnosis dates for privacy purposes). There are almost 200 cases in the database; therefore almost "600 controls"

ID Case Number Sampling Date Randomized P_1WK P_2WK P_3WK P_4WK P_5WK P_1WK_FWD TAVG_1WK TAVG_2WK TAVG_3WK TAVG_4WK TAVG_5WK TAVG_1WK_FWD
1 1 1A A 1 1.34 0.87 0.87 0 0.67 81.57 82.71 85.29 81.29 78.14
1 0 1B B 1 0 86.71
1 0 1C C 1 0.55 83.29
1 0 1D D 1 2.8 83
2 1 2A A 2 1.52 0.68 1.06 0.28 0.08 79.43 79.43 79.86 83.14 77.43
2 0 2B B 2 0 73.57
2 0 2C C 2 0.12 84.71
2 0 2D D 2 0 83.57
3 1 3A A 3 0.08 1.5 0.95 0.36 0.16 82 77.71 79.57 82.57 81
3 0 3B B 3 0 81.57
3 0 3C C 3 1.22 74.29
3 0 3D D 3 5.04 80.29
4 1 4A A 4 2.48 0.08 1.74 1.07 2.17 83.43 82 77.71 79.57 82.57
4 0 4B B 4 0 81.71
4 0 4C C 4 0.08 68.29
4 0 4D D 4 0 80.43
5 1 5A A 1 2.1 0.67 1.52 1.39 0.13 86.36 86 80.29 81.86 85.29
5 0 5B B 1 0 83.57
5 0 5C C 1 3.64 83.29
5 0 5D D 1 1.25 83