Assessment of long‐term survival of cancer patients using cancer registry data from eastern China: Period analysis is superior to traditional methods

2020 ◽  
Vol 147 (4) ◽  
pp. 996-1005
Author(s):  
Xiyi Jiang ◽  
Liangyou Wang ◽  
Yongran Cheng ◽  
Huijuan Tang ◽  
Tianhui Chen
Keyword(s):  
Cancer Patients ◽  
Cancer Registry ◽  
Eastern China ◽  
Registry Data ◽  
Traditional Methods ◽  
Term Survival ◽  
Period Analysis ◽  
Long Term Survival ◽  
Cancer Registry Data

scholarly journals Long‐term survival and conditional survival of cancer patients in Japan using population‐based cancer registry data

2014 ◽  
Vol 105 (11) ◽  
pp. 1480-1486 ◽  
Author(s):  
Yuri Ito ◽  
Isao Miyashiro ◽  
Hidemi Ito ◽  
Satoyo Hosono ◽  
Dai Chihara ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Cancer Registry ◽  
Population Based ◽  
Registry Data ◽  
Conditional Survival ◽  
Term Survival ◽  
Long Term Survival ◽  
Cancer Registry Data

scholarly journals Using Population-Based Cancer Registration Data and Period Analysis to Accurately Assess and Predict 5-Year Relative Survival for Lung Cancer Patients in Eastern China

2021 ◽  
Vol 11 ◽  
Author(s):  
Runhua Li ◽  
Min Zhang ◽  
Yongran Cheng ◽  
Xiyi Jiang ◽  
Huijuan Tang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Eastern China ◽  
Relative Survival ◽  
Population Based ◽  
Term Survival ◽  
Period Analysis ◽  
Long Term Survival ◽  
Lung Cancer Patients

BackgroundThe assessment of long-term survival of lung cancer patients based on data from population-based caner registries, using period analysis, was scarce in China. We aimed to accurately assess the long-term survival of lung cancer patients, and to predict the long-term survival in the future, using cancer registry data from Taizhou City, eastern China.MethodsFour cancer registries with high-quality data were selected. Patients diagnosed with lung cancer during 2004–2018 were included. The long-term survival was evaluated using period analysis, with further stratification by sex, age at diagnosis and region. Additionally, projected 5-year relative survival (RS) of lung cancer patients for 2019-2023 was evaluated, using model-based period analysis.ResultsThe 5-year RS of lung cancer patients diagnosed during 2014–2018 was 40.2% (31.5% for men and 56.2% for women). A moderate age gradient was observed for the period estimate, with the estimate decreasing from 50.5 to 26.5% in the age group of 15–44 years and ≥75 years, respectively. The 5-year RS of urban area was higher than that of rural area (52.3% vs. 38.9%). The overall projected 5-year RS of lung cancer patients was 52.7% for 2019–2023, with estimate of 43.0 and 73.2% for men and women, respectively. A moderate age gradient was also observed for the projected estimate. Moreover, estimate reached nearly 50% for rural and urban areas.ConclusionPeriod analysis tended to provide the up-to-date and precise survival estimates for lung cancer patients, which is worth further application, and provides important evidence for prevention and intervention of lung cancer.

More Up-To-Date Monitoring of Long-Term Survival Rates by Cancer Registries: An Empirical Example

2001 ◽  
Vol 40 (03) ◽  
pp. 248-252 ◽  
Author(s):  
O. Gefeller ◽  
C. Stegmaier ◽  
H. Ziegler ◽  
H. Brenner
Keyword(s):  
Cancer Patients ◽  
Survival Rates ◽  
Cancer Registries ◽  
Newly Diagnosed ◽  
Recent Improvement ◽  
Term Survival ◽  
Period Analysis ◽  
Long Term Survival ◽  
Survival Expectations

Abstract:A few years ago, a new method of survival analysis, denoted period analysis, has been developed to derive more up-to-date estimates of long-term survival rates of cancer patients, but this method has rarely been applied so far. Using empirical examples from the Saarland Cancer Registry, we illustrate how seriously traditional long-term survival estimates may lag behind survival expectations of newly diagnosed cancer patients in the case of recent improvement in prognosis, and to what extent this problem may be reduced by period analysis of survival. We conclude that period analysis should be more widely used for deriving more up-to-date long-term survival estimates.

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