RDU Webinar Series: Online Interactive Tool to Improve the Understanding of Survival Statistics

Recorded On: 09/14/2017


There are a variety of ways to quantify cancer survival with each measure having advantages and disadvantages. For example, relative/net survival is useful for making fair comparisons between population groups and over time, but is of less relevance to clinicians or patients. The differences between the various measures and how they should be interpreted has led to confusion among scientists, the media, health care professionals and patients.

Purpose and methods.

We have developed an online interactive tool to help improve the understanding of a variety of cancer survival measures and how these vary between patients. Its primary purpose is to function as an aid in the interpretation of a variety of commonly reported, important and more complex cancer survival measures that are available from fitting statistical models. The interpretation is facilitated through the use of dynamic interactive graphics available using an online interactive tool. The interactivity improves understanding of these measures and how survival or mortality may vary by age and sex. Routine measures of cancer survival are reported, such as net and all-cause survival. In addition, individualised estimates using crude probabilities are often more appropriate for patients or health care professionals. The results are presented in a variety of ways, including graphs, “people charts”, tables and descriptive text using natural frequencies. All results are updated immediately when using drag bars, drop-down menus or radio buttons. This immediate feedback together with the simple text descriptions leads to both better understanding of individual risk and the differences between the various measures.

Results and conclusion

The online tool is in final testing using English data for a range of cancer sites. The tool is available at www.interpret.le.ac.uk.  We have plans to further develop the interactive tool by incorporating data from different countries and from statistical models that incorporate more disease characteristics (e.g. stage, grade and tumour size).  

Paul C Lambert

Paul C Lambert is a professor of Biostatistics working in the Biostatistics Research Group in the Department of Health Sciences at the University of Leicester, UK. He also part-time in the Biostatistics Group in the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.


Online Interactive Tool to Improve the Understanding of Cancer Survival Statistics