This is not a conventional 'repeated measures' design. This is a 'survival' study. The outcome (response) is not alive or dead. Death is the 'event.' The response is the time to this event (day). The fact that some subjects are still alive means that the last time observed is a minimum life time. Such data is 'censored.' You could use 'right censored' data in JMP (only lower bound). The fact that you do not continuously monitor the subject but only once a day suggests that you should use 'interval censoring' to obtain more accurate estimates (lower and upper bound).
Build your table so that you have two columns for the outcome (day). The first column is the time that they were last observed alive and the second column is the time that they were first observed dead. The fact that you observed a subject for many days is irrelevant. It is only the time to death that matters.
You set up your data table with one observation for each subject including the covariates for the last observation. It might look like this for the first few subjects. (If I understand your example.)
To analyze the data, select Analyze > Fit Model. Click the drop down menu for Personality and select Parametric Survival. Select the Early and Late data columns and click Y. NOTE: it is essential that the earliest time be entered first. Select the covariate data columns and use Add or Macros to make the linear predictor. It should look something like this:
I recommend that you do not use both Variable 2 and Variable co in the model together.