prognostication model with spurious results I am reviewing a paper on prognostication after bleeding where the authors present two different logistic regression models; one for survival and one for an unfavorable outcome (dependent living and death). Plugging in the exact same values for the predictive variables (age, hypertension) and using the beta values and intercept from the provided models I get
inv.logit(-4.918+.32*5+.327+2.786)=.45 for mortality

and
inv.logit(-3.703+.034*5+.268+2.565)=.33 for unfavorable outcome.

Since the unfavorable outcome encapsulates mortality, I expect the probability to be higher. Do you see any other explanation for this apart from a mix-up in the coding of the dependent variable? So that the probs should have been .55 and .77 respectively instead ?

Assuming that mortality is an unfavourable outcome and that both models are estimated from the same dataset, then yes it should be the case that the probability for unfavourable outcome conditioned on data should be larger than the probability of mortality.
You can actually see this reflected in the intercept.  The intercept for mortality is smaller than that of unfavourable outcome, meaning the former has a smaller probability than the latter.
However, the two linear predictors you use do not seem to be the same.  Differences in the intercept are expected, but your predictors look different.  It isn't clear which of those numbers are the coefficients and which are the predictors, so I can't say yet if this is where the problem is.  It might be a good place to start though.
