Hi @Tarek, is the metric a calculated value or is it a raw (base) value?

Unfortunately, I do not. We use R exclusively where I work. Sorry, mate!

@RichardHardy that makes sense. I will consider this question answered! I am new to Cross Validated, will you post your comment in a formal answer and then I mark it as answered? Or is the comment enough?

@RichardHardy, that makes sense. I would force the intercept to be the starting blood glucose level and then use time intervals as regressors after the injection. I received other advice from an experienced statistician, though, who said that he was not sure time series type regression would be best in a lag event since the occurrence is basically a status change. He says I could treat the pre-post of the effect like transferring from group A to group B (this supposes that there are at least 2 continuous measures though, i.e. a criterion and an indicator). What do you think of that approach?

Great! For others viewing your question, please upvote my comment so that they know this is a valuable resource, and happy forecasting. :)

@Conta, thanks for clarifying. If I interpret your comment correctly, you are interested in localized autocovariance estimation of segments, which can allow you to determine at what point that a time series achieves stationarity. There is a terrific article on this at maths.bris.ac.uk/~guy/Research/LSTS/LACF.html. I strongly recommend a thorough reading. The R package 'locits' will provide and plot the estimates using wavelet processes. I would say do NOT parse the series to try and see when it is stationary because that is a method that is too arbitrary.