What can we infer from proportional hazards?

Colleagues and I recently wrote a letter to the editor regarding difficulties of interpreting period specific hazard ratios from randomised trials as representing solely changes in treatment effect over time, as discussed in a previous post. The authors whose paper we were writing about responded to our letter. One of their points was the following:

Note that in a randomized controlled trial, if the proportional hazards assumption holds and there are no unobserved confounders (i.e., the patient population is homogeneous and there is no differential treatment effect across different subpopulations), a HR generated by Cox regression with treatment alone as a single covariate does have a causal interpretation. The hazard functions in this case do not depend on any confounder.

It is fairly implausible in any setting that a patient’s hazard does not depend on any of their characteristics. Indeed it is because we often have some understanding of what these variables are that they are used in stratified randomisation schemes and in the statistical analysis of the trial’s data.

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Testing equality of two survival distributions: log-rank/Cox versus RMST

Cox’s proportional hazards model is by far the most common approach used to model survival or time to event data. For a simple two group comparison, such as in a randomised controlled trial, the model says that the hazard of failure in one group is a constant ratio (over time) of the hazard of failure in the other group. A test that this hazard ratio equals 1 is a test of the null hypothesis of equality of the survival functions of the two groups. The log rank test is essentially equivalent to the score test that the HR=1 in the Cox model, and is commonly used as the primary analysis hypothesis test in randomised trials.

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