Hazard ratios and their causal interpretation: challenges and guidance

The hazard ratio, typically estimated using Cox's famous proportional hazards model, is the most common effect measure used to describe the association or effect of a covariate on a time-to-event outcome. In recent years, the hazard ratio has been argued by some to lack a causal interpretation, even in randomized trials, and even if the proportional hazards assumption holds. This is concerning, not least due to the ubiquity of hazard ratios in analyses of time-to-event data. We review these critiques and describe how we think hazard ratios should be interpreted. In light of the issues involved, alternative measures may be preferable to describe effects of exposures or treatments on time-to-event outcomes.