Interpreting Time-to-Event Analysis

I need to superficially understand an applied statistical approach in a research paper for a class: The Effect of Spironolactone on Ventricular Tachyarrhythmias in Patients With Implantable Cardioverter-Defibrillators
My understanding of the primary result is the following: Survival probabilities were estimated with Kaplan-Meier curves, which had a log-rank hazard ratio of 1.01 (95% CI: 0.64, 1.83). Since the hazard ratio CI includes one, patients receiving the drug had an equal risk of experiencing VT/VF at any given time than those who were not treated. Ratio of median endpoint times was also derived from the model, which I calculated to be 1.4 (95% CI: 0, 1.03). Since the median endpoint ratio CI includes one, the time to a VT/VT episode in the treated group was no different from that of the control. Thus, no difference was found between spironolactone and a placebo in reducing the incidence of VT/VF in this clinical trial.
Am I on the right track here?


Less is more. Stay pure. Stay poor.
Well we get stuck with saying we failed to find a difference, since equivalence requires a particular test and usually a threshold margin to say how different they could be and still be considered equivalent. However with the HR of 1.01, it seems like it wouldn't matter how large of a sample you had it would be hard to prove 1.01 is different from '1' and if it did prove out - would it be clinically meaningful. Probably not.