Modelling population‐based cancer survival trends by using join point models for grouped survival data

Binbing Yu, Lan Huang, Ram C. Tiwari, Eric J. Feuer and Karen A. Johnson

Journal of the Royal Statistical Society Series A, 2009, vol. 172, issue 2, 405-425

Abstract: Summary. In the USA cancer as a whole is the second leading cause of death and a major burden to health care; thus medical progress against cancer is a major public health goal. There are many individual studies to suggest that cancer treatment breakthroughs and early diagnosis have significantly improved the prognosis of cancer patients. To understand better the relationship between medical improvements and the survival experience for the patient population at large, it is useful to evaluate cancer survival trends on the population level, e.g. to find out when and how much the cancer survival rates changed. We analyse population‐based grouped cancer survival data by incorporating join points into the survival models. A join point survival model facilitates the identification of trends with significant change‐points in cancer survival, when related to cancer treatments or interventions. The Bayesian information criterion is used to select the number of join points. The performance of the join point survival models is evaluated with respect to cancer prognosis, join point locations, annual percentage changes in death rates by year of diagnosis and sample sizes through intensive simulation studies. The model is then applied to grouped relative survival data for several major cancer sites from the ‘Surveillance, epidemiology and end results’ programme of the National Cancer Institute. The change‐points in the survival trends for several major cancer sites are identified and the potential driving forces behind such change‐points are discussed.

Date: 2009
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