Median Survival Definition and Meaning for People with Cancer

Comparing Median Survival to Average Survival

graph showing the median
What is the definition of the term median survival?. Flickr.com/Creative Commons

Definition: Median Survival

Median survival is defined as the time after which 50 percent of people with a particular condition are still living, and 50 percent have died. For example, a median survival of 6 months would indicate that after 6 months, 50 percent of people with that condition would be alive, and 50 percent would have passed away.

When is the Term Median Survival Used?

There are many ways in which you may hear the term median survival used:

  • As a description of the benefits of a treatment.  
  • As an estimate of the prognosis of a condition. For example, median survival may be used to describe the prognosis of a disease in which the survival rate is fairly short. How long do people usually live?
  • As an endpoint in a clinical trial.

Comparing and Contrasting Median Survival to Other Statistics

Median survival is used to talk about many treatments for cancer. It can be a better estimate than the average survival rate (the average length of time someone lives for example) when there is a wide variation in how people respond to a condition or treatment.

A few other statistical terms you may hear include survival rate, progression free survival and more, which are defined in this article.

Advantages and Disadvantages of Using Median Survival with Cancer

Without going into a discussion of statistics, it's important to note that any statistic has drawbacks when describing the life expectancy of a cancer, or the benefit of a treatment.

A few examples are mentioned below.

  • An Advantage - For a treatment that extends survival by days or weeks or even months, the median survival time may give a better indication of how the treatment works. For example, a hypothetical treatment may increase the median survival time by 4 months - for example, half of the people might live for 16 months rather than 12 months with the treatment. Since most people would not survive long term, estimates such as the 5-year survival rate or even the 2-year survival rate would not reveal the potential of the treatment to give people 4 extra (and hopefully good) months to live.
  • A Disadvantage - A disadvantage would occur if a treatment resulted in very good long term results, but for less than half of the people, down the line. If over half of people died in the first 2 years the median survival would be less than 2 years. In this case, perhaps a hypothetical treatment, if tolerated in the first 2 years could result in longer survival. In this fictional example, it could be that 30 percent of people lived for 5 years after the treatment whereas only 5 percent lived that long without the treatment. In this case, the 5-year survival rate would say more about the potential of the treatment than median survival.

Statistics are Numbers NOT People

It's extremely important to make note that statistics of any kind are simply numbers. People vary widely in how they respond to treatments and how long they live with various treatments.  There are many factors which may raise or lessen someone's chance of survival with cancer.

It's also critical to note that any statistics you hear about cancer are often a few years old.

Progress is being made in cancer treatment. The oft-quoted survival statistics for lung cancer are 5 years old.  That said, there were more treatments approved for lung cancer in the period from 2011 to 2015, than in the 40 year period prior to 2011. This is just one of many reasons to hang on to hope.

Examples:

Jack was told that the median survival for people with stage 3B lung cancer is 13 months. This would mean that, statistically, he had about a 50 percent chance of being alive with his disease in 13 months.

Sources:

Rao, S., and D. Schoenfeld. Statistical Primer for Cardiovascular Research. Survival Methods. Circulation. 2007. 115:109-113.

Rich, J. et al. A Practical Guide to Understanding Kaplan-Meier Curves. Otolaryngology Head and Neck Surgery. 2010. 143(3):331-336.

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