# How to interpret a hazard ratio from a continuous variable — unit of difference?

I am reading an article which shows Hazard Ratios for continuous variables, but I'm not sure how to interpret the given values.

My current understanding of hazard ratios is that the number represents the relative likelihood of [event] given some condition. E.g: if the hazard ratio for death from lung cancer given smoking (a binary event) is 2, then smokers were twice as likely to die in the monitored time period than non-smokers.

Looking on wikipedia, the interpretation for continuous variables is that the hazard ratio applies to a unit of difference. This makes sense to me for ordinal variables (e.g number of cigarettes smoked a day), but I don't know how to apply this concept to continuous variables (e.g. grams of nicotine smoked a day?)

The R rms package's cph and summary functions compute, by default, the inter-quartile-range hazard ratio. This handles nonlinearities (but not non-monotonicity) and interactions fairly easily, putting almost all variables on an equal basis.
• If there is more than one coefficient for the predictor in the model, you can't interpret any single coefficient very well. A simple case would be having $x$ and $x^2$ in the model; you need to vary $\beta_{1}x + \beta_{2}x^{2}$ to get a hazard ratio of interest. – Frank Harrell Sep 23 '13 at 15:30