{"id":374,"date":"2019-04-05T11:43:26","date_gmt":"2019-04-05T09:43:26","guid":{"rendered":"https:\/\/www.statsimprove.com\/en\/?p=374"},"modified":"2019-04-05T11:43:54","modified_gmt":"2019-04-05T09:43:54","slug":"how-to-interpret-the-value-of-hazard-ratio-in-practice","status":"publish","type":"post","link":"https:\/\/www.statsimprove.com\/en\/how-to-interpret-the-value-of-hazard-ratio-in-practice\/","title":{"rendered":"How to interpret the value of ‘Hazard Ratio” in practice?"},"content":{"rendered":"

The Hazard ratio (HR) is one of the measures that in clinical research are most often difficult to interpret for students and researchers. In this post we will try to explain this measure in terms of its practical use.<\/strong><\/p>\n

You should know what the Hazard Ratio is, but we will repeat it again.<\/p>\n

Let’s take as an example a study in which I want to evaluate the survival of patients exposed to smoke compared to those exposed to high-calorie diet.<\/strong><\/p>\n

In this case the Hazard Ratio is<\/strong> the ratio between the mortality rate in the group of patients exposed to smoke and the mortality rate in the mortality rate in the group of subjects exposed to high-calorie diet.
\nIf you don’t like the definition “mortality rate”, you can interpret it as a “speed of death”, even if it’s not quite the same thing.<\/p>\n

I’m not going to explain how to calculate the Hazard Ratio because this article is not about it. We are interested here in its interpretation and the way it should be reported in the literature.<\/p>\n

Let\u2019s go ahead.<\/strong><\/p>\n

Let’s say for example that you have estimated the hazard ratio between the experimental and the control groups using a statistical model (a classic example: a Cox model) and its value, let\u2019s say, is 2.2.<\/strong><\/p>\n

How can we report and interpret the value of 2.2 in terms of practical use?<\/strong><\/p>\n