Cancer Trends Progress Report

Five-year relative cancer survival: The proportion of patients surviving cancer 5 years after diagnosis calculated in the absence of other causes of death. The relative survival ratio is defined as the observed survival in the patient group divided by the expected survival of a comparable group from the general population. This ratio represents survivors that are expected if cancer were the only cause of death in the cohort.

Characterizing changes in cancer survival over time: The JPSurv (Joinpoint Survival Model) software (1,2) has been developed to analyze trends in survival with respect to year of diagnosis. The software finds the location and number of joinpoints (years of diagnosis at which survival trends have changed) that best fit the data. The estimates in this report use annual relative survival data with up to 5 years of follow-up for cancer patients diagnosed at each calendar year from 2000 through 2018. To summarize the trends in each segment between joinpoints, we use the average absolute change in survival (AAC_S). The AAC_S represents the average percentage points difference of cancer survival for people diagnosed in one calendar year compared to the prior year. For example, a 5-year survival increase from 50% to 60% for people diagnosed in 1980 versus 1990, represents a total increase of 10 percentage points in 10 years and an AAC_S of 1 percentage (survival) points for people diagnosed in a year compared to the previous between 1980 and 1990. The AAC_S depends on the follow-up survival time.

The joinpoint survival model (3) is an extension of the proportional hazard model for survival where the effect of calendar year at diagnosis is linear on the log hazard of cancer death scale. The joinpoint survival model allows for different linear trends between joinpoints, i.e., calendar years where trends in the hazard of cancer death changes. However, caution should be used when interpreting survival trends for cancer sites for which screening have been widely disseminated (4). See the following articles for more details.

1. The JPSurv software: https://analysistools.cancer.gov/jpsurv/
2. Mariotto AB, Zhang F, Buckman DW, et al. Characterizing Trends in Cancer Patients' Survival Using the JPSurv Software. Mariotto et al. Cancer Epidemiol Biomarkers Prev. 30 (11): 2001–2009. https://doi.org/10.1158/1055-9965.EPI-21-0423
3. Yu BB, Huang L, Tiwari RC, Feuer EJ, Johnson KA. Modelling population-based cancer survival trends by using join point models for grouped survival data. Yu et al. Journal of the Royal Statistical Society Series a-Statistics in Society. 2009;172:405-25.
4. Cho H, Mariotto AB, Schwartz LM, Luo J, Woloshin S. When do changes in cancer survival mean progress? The insight from population incidence and mortality. J Natl Cancer Inst Monogr. 2014 Nov;2014(49):187-97. doi: 10.1093/jncimonographs/lgu014. PMID: 25417232; PMCID: PMC4841163.