scholarly journals Use of period analysis for providing more up-to-date estimates of long-term survival rates: empirical evaluation among 370 000 cancer patients in Finland

2002 ◽  
Vol 31 (2) ◽  
pp. 456-462 ◽  
Author(s):  
H. Brenner
Keyword(s):  
Cancer Patients ◽  
Survival Rates ◽  
Empirical Evaluation ◽  
Term Survival ◽  
Period Analysis ◽  
Long Term Survival

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.

Long-term survival rates of older breast cancer patients: Population-based estimates over three decades.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 9051-9051
Author(s):  
B. Kanapuru ◽  
A. Jemal ◽  
C. S. Hesdorffer ◽  
W. B. Ershler ◽  
J. W. Yates
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Survival Rates ◽  
Population Based ◽  
Breast Cancer Patients ◽  
Term Survival ◽  
Long Term Survival

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.

Effect of pre-treatment anxiety and depression on long term survival of prostate cancer patients treated radically with radiation and hormone therapy.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e23087-e23087
Author(s):  
Lauren Jones ◽  
Micheal O'Cathail ◽  
Shaymaa Usama Hosni ◽  
Jessica Little ◽  
Ashley Cox ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Survival Rates ◽  
Anxiety And Depression ◽  
Term Survival ◽  
Long Term Survival ◽  
Cox Regression Analysis ◽  
T Stage

e23087 Background: The evidence for effect of anxiety and depression on cancer survival rates is contradictory (Ref : Prostate-JCO 2014.32:2471, Breast-JCO 1987. 5:768, Lung-JPSM 2017.53:1057). We assessed the effect of prospectively collected HADS score (Hospital Anxiety and Depression Scale) on subsequent long term survival. Methods: After ethics approval & informed consent, 145 prostate cancer patients (pts) were enrolled, onto a QOL study. HADS questionnaire was prospectively completed at baseline & at 3 mths after start of androgen deprivation therapy (ADT). Survival data collected in Jan 2019. PATIENT CHARACTERISTICS: Median age 69 (range 50 to 78); T stage T2:63.5%, T3:28.4%. Gleason 7 in 53.4% and 8-10 in 39.2%; Median PSA 10.2 (0.3 to 102); Median testosterone 11.6 (0.6 to 26.7); ADT duration median 6 mths (2 to 60 mths); Median overall survival 10.11 yrs Results: At baseline, HADS anxiety scores normal (0-7) in 78%; abnormality mild (8-10) in 12%; moderate (11-14) in 6% and severe (15-21) in 2%.. At > 3 mths, respective anxiety scores (%) were 71.3, 17.5, 7.7 and 3.5. At baseline, HADS depression scores (%) were 91.2, 5.2, 1.4 and 0.7. At > 3 mths, respective depression scores were (%) 83.9, 11.2, 3.5 and 1.4 Although median survival was numerically lower in pts scoring high on HADS at baseline, the survival rates were not statistically significant on Log rank test. Moreover, on Cox regression analysis with Age, Gleason Score, T Stage, baseline PSA as co-variates, there was no increased hazard from higher HADS scores (Table). Conclusions: Biological factors probably outweigh the influence of psychological factors on overall survival. [Table: see text]

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