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

Author

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

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.

Suggested Citation

  • Binbing Yu & Lan Huang & Ram C. Tiwari & Eric J. Feuer & Karen A. Johnson, 2009. "Modelling population‐based cancer survival trends by using join point models for grouped survival data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 405-425, April.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:2:p:405-425
    DOI: 10.1111/j.1467-985X.2009.00580.x
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    References listed on IDEAS

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    1. Percy, C. & Stanek III, E. & Gloeckler, L., 1981. "Accuracy of cancer death certificates and its effect on cancer mortality statistics," American Journal of Public Health, American Public Health Association, vol. 71(3), pages 242-250.
    2. E. A. Weller & E. J. Feuer & C. M. Frey & M. N. Wesley, 1999. "Parametric relative survival regression using generalized linear models with application to Hodgkin's lymphoma," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(1), pages 79-89.
    3. Ram C. Tiwari & Kathleen A. Cronin & William Davis & Eric J. Feuer & Binbing Yu & Siddhartha Chib, 2005. "Bayesian model selection for join point regression with application to age‐adjusted cancer rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(5), pages 919-939, November.
    4. P. M. Lerman, 1980. "Fitting Segmented Regression Models by Grid Search," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 77-84, March.
    5. Yuhong Yang, 2005. "Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation," Biometrika, Biometrika Trust, vol. 92(4), pages 937-950, December.
    6. Els Goetghebeur & Louise Ryan, 2000. "Semiparametric Regression Analysis of Interval-Censored Data," Biometrics, The International Biometric Society, vol. 56(4), pages 1139-1144, December.
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