Median Survival for Cancer Patients

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

Median survival is defined as the time after which 50% of people with a particular condition are still living, and 50% have died. For example, a median survival of 6 months would indicate that after 6 months, 50% of people with that condition would be alive, and 50% would have passed away. Median survival is often used as an endpoint in clinical trials, but is also used frequently to describe prognosis in advanced cancers. Learn how the term is used, the advantages and disadvantages, and what you should know about statistics such as median survival if you or a loved one are living with cancer.

Median Survival Overview

If you've looked at cancer studies, you may see references to both survival rate and median survival. This can be confusing as they are different measurements, and can make it challenging to compare different research studies.

When the Term Median Survival Might Be Used 

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

  • As a description of the benefits of a treatment. In some settings, the median survival with a particular therapy is a better way to know what to expect than, for example, the 1-year or 5-year survival rate.
  • 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 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. In addition, with newer treatments, information on 5-year or even 1-year survival rates may not be known.

Advantages and Disadvantages

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

Advantages of Using the Term

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.

Another example of the benefit of using the term median survival is with regard to differences in cancers on a molecular level. Two cancers of the same type and subtype may be very different on a molecular level. An example would be that of stage IV non-small cell lung cancer, specifically lung adenocarcinoma. The average 5-year survival rate is quoted at being roughly 10% at most (which has increased from only 1% to 2% not long ago). That said, for people who have a specific molecular subtype of this disease (ALK positive lung cancer), a study published in 2019 found that the median survival with appropriate treatment was 6.8 years.

Disadvantages of Using the Term

An example of a disadvantage of the term median survival is if a treatment results in very good long-term results for some people, but less than 50%. 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 year,s could result in longer survival. For example, it could be that 30% of people lived for 5 years after the treatment whereas only 5% lived that long without the treatment. In this case, the 5-year survival rate would say much more about the potential of the treatment than the median survival.

A real-life example of this is with some forms of immunotherapy. In this case, a drug may not extend median survival to a very significant degree, but some people who would otherwise have been expected to pass away may achieve long term control of their cancer (what is referred to as a durable response).

Statistical vs. Clinical Significance

It's important to note that statistical significance (the results you may read about in a study) and clinical significance are not the same things. Statistical significance (say, how excited researchers may get from the result of a study) gives information about the reliability of a study, whereas clinical significance describes how important this is for individual people. There are many variables that must be considered, such as the extent of a change in median survival, the tolerability of the treatment that changes median survival, as well as the toxicity.

An example that has been cited is that of a few targeted drugs used for pancreatic cancer. A study that showed a specific combination of medications increased median survival from 5.91 months to 6.24 months was very statistically significant, but not so much clinically. In this example, the clinical significance was that the people lived, on average, 10 more days, while also suffering the side effects and cost of the treatment.

In other cases, a study may not have great statistical significance but may have very significant clinical differences; people would experience significant improvement.

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 that 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. If the first line treatments for the disease have changed, statistics simply tell a person the average prognosis of a person treated with a different treatment than they are receiving.

Example:

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. Since cancer treatments are improving, however, the median survival rate in this case may not be an accurate prediction. The statistic would tell Jack the amount of time after which half of people had passed away from his disease but received different treatments.

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  1. Pacheco J, Gao D, Smith D, et al. Natural History and Factors Associated with Overall Survival in Stage IV ALK Rearranged Non-Small-Cell Lung CancerJournal of Thoracic Oncology. 2019. 14(4):691-670. doi:10.1016/j.jtho.2018.12.014

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