In joint models for longitudinal and time-to-event data, all the models that I have seen assume that there is at least one observation for each subject. I am also aware of the principal strata effects when the outcome is truncated by death. However, I could not find any longitudinal model when there are subjects with no observation because of death. I really appreciate it if you can help me find relevant papers.

I hope that someone who is versed with joint models will answer. I personally would try to use a state transition model for this problem so that it is easy to (1) model death and (2) interpret the results. A discrete-time ordinal longitudinal model based on a Markov process is a good candidate for that. Multiple examples are here. State transition models are unsurpassed for handling absorbing states IMHO. No principal strata needed.

Thank you so much for the examples.

are you asking out of curiosity or you have data of this kind? if the latter i wonder if they were scheduled visits? and some small proportion did not make it to the first visit? or the data aren’t from a clinical trial?

This is an observational longitudinal cohort study where almost 20 percent of subjects died before the first measurement.

in this open access paper see table 1 in the pdf file under ‘web material’:

they have 265 and 294 people in the two groups at baseline and a1c is first measured at 2 years where there are 181 and 214 people. I don’t know what proportion of this is mortality, but surely some … I’m too rushed to check the detail at the moment

edit: actually they are censored by time to first cvd event (the outcome), not mortality, but it’s perfectly analogous to your problem it seems

Thank you so much. It seems that they have baseline A1c measurements for the cases (Figure 2). Frankly, the more I read the more I believe that joint models cannot handle this situation.

With an incidence of death of 0.2 it is hard not to make death a formal part of the outcome. Longitudinal ordinal models can do just that.

Yes, I am working on it. Thank you again for your great suggestions.