# How to interpret hazard ratios for a cox model with 100% failure rate

I am studying the impact of temperature on the onset of flowering of trees. I am using a cox model for time dependent covariates as implemented in the R package survival. I only have one (cumulative) time dependent covariate, that is daily growing degree days (GDD, calculated from daily min and max temperature).

My results look something like this:

     coef exp(coef)  se(coef)     z Pr(>|z|)
0.0117237 1.0117927 0.0004184 28.02   <2e-16 ***


Now my question: I understand a hazard ratio of 1.01 would mean that a unit increase in my time dependent covariate increases the risk of death by 1%, however, in my case all trees are expected to flower.

What I want to know is whether a unit increase in my time dependent covariates causes earlier flowering. I this information contained in the hazard ratio, or am I doing something wrong?

Whether or not all observations are uncensored does not affect the interpretation of a hazard ratio. The hazard ratio is an instantaneous event rate, roughly speaking the probability of having an event within a small interval of time around time $t$ divided by the probability of not having the event before time $t$. Hazard ratios are ratios of such instantaneous hazard rates. So a baseline or time-dependent covariate can modify the hazard rate of the ultimate outcome.