I am wondering if ordinal regression (or any of the other methods mentioned in this chapter) would be appropriate if there is a time component. In survival analysis, like Cox regression, we predict time to a binary (or I think sometimes ordinal) event. Is there an equivalent analysis method for when you have a continuous outcome?
My data: Cohort study with 3 waves (~ 5 years between each wave). I am using covariates measured at baseline to predict a continuous outcome at an individual’s last follow-up. From what I have seen, all models doing similar analyses end up dichotomizing the outcome and using a Cox or Weibull model.
Based on the discussion in this post, I believe I can’t add follow-up time to the predictors as it was not measured at baseline and is considered an outcome variable. But I have been unable to find an equivalent to survival analysis for continuous outcomes.
Perhaps, since there is no “event” per se, we don’t have to worry about censoring and can simply ignore the follow-up time? The issue I have then is I’m not sure how useful a model without a time horizon would be.
Maybe I could say this model predicts the 15-year outcome? In that case, I’m not sure if I should impute wave 3 outcome for those who were lost to follow-up after wave 2. I know that predicting Y is not usually recommended so I wonder if it’s better to just ignore those with missing wave 3 outcome.
I hope this doesn’t read too much like a train of thought. I’d be happy to further clarify anything.