Literature DB >> 31221717

Differences in Cancer Survival with Relative versus Cause-Specific Approaches: An Update Using More Accurate Life Tables.

Gonçalo Forjaz de Lacerda1,2, Nadia Howlader3, Angela B Mariotto3.   

Abstract

BACKGROUND: We investigated differences in net cancer survival (survival observed if the only possible cause of death was the cancer under study) estimated using new approaches for relative survival (RS) and cause-specific survival (CSS).
METHODS: We used SEER data for patients diagnosed in 2000 to 2013, followed-up through December 31, 2014. For RS, we used new life tables accounting for geography and socio-economic status. For CSS, we used the SEER cause of death algorithm for attributing cancer-specific death. Estimates were compared by site, age, stage, race, and time since diagnosis.
RESULTS: Differences between 5-year RS and CSS were generally small. RS was always higher in screen-detectable cancers, for example, female breast (89.2% vs. 87.8%) and prostate (98.5% vs. 93.7%) cancers; differences increased with age or time since diagnosis. CSS was usually higher in the remaining cancer sites, particularly those related to specific risk factors, for example, cervix (70.9% vs. 68.3%) and liver (20.7% vs. 17.1%) cancers. For most cancer sites, the gap between estimates was smaller with more advanced stage.
Conclusion: RS is the preferred approach to report cancer survival from registry data because cause of death may be inaccurate, particularly for older patients and long-term survivors as comorbidities increase challenges in determining cause of death. However, CSS proved to be more reliable in patients diagnosed with localized disease or cancers related to specific risk factors as general population life tables may not capture other causes of mortality. IMPACT: Different approaches for net survival estimation should be considered depending on cancer under study. ©2019 American Association for Cancer Research.

Entities:  

Mesh:

Year:  2019        PMID: 31221717      PMCID: PMC6726514          DOI: 10.1158/1055-9965.EPI-19-0125

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  26 in total

1.  Non-parametric comparison of relative versus cause-specific survival in Surveillance, Epidemiology and End Results (SEER) programme breast cancer patients.

Authors:  J W Gamel; R L Vogel
Journal:  Stat Methods Med Res       Date:  2001-10       Impact factor: 3.021

2.  The relative survival rate: a statistical methodology.

Authors:  F EDERER; L M AXTELL; S J CUTLER
Journal:  Natl Cancer Inst Monogr       Date:  1961-09

3.  Evidence of a healthy volunteer effect in the prostate, lung, colorectal, and ovarian cancer screening trial.

Authors:  P F Pinsky; A Miller; B S Kramer; T Church; D Reding; P Prorok; E Gelmann; R E Schoen; S Buys; R B Hayes; C D Berg
Journal:  Am J Epidemiol       Date:  2007-01-22       Impact factor: 4.897

Review 4.  Interpreting trends in cancer patient survival.

Authors:  P W Dickman; H-O Adami
Journal:  J Intern Med       Date:  2006-08       Impact factor: 8.989

5.  Estimating expected survival probabilities for relative survival analysis--exploring the impact of including cancer patient mortality in the calculations.

Authors:  Mats Talbäck; Paul W Dickman
Journal:  Eur J Cancer       Date:  2011-09-15       Impact factor: 9.162

6.  Measuring cancer survival in populations: relative survival vs cancer-specific survival.

Authors:  Diana Sarfati; Tony Blakely; Neil Pearce
Journal:  Int J Epidemiol       Date:  2010-02-08       Impact factor: 7.196

7.  Early death rate in acute promyelocytic leukemia remains high despite all-trans retinoic acid.

Authors:  Jae H Park; Baozhen Qiao; Katherine S Panageas; Maria J Schymura; Joseph G Jurcic; Todd L Rosenblat; Jessica K Altman; Dan Douer; Jacob M Rowe; Martin S Tallman
Journal:  Blood       Date:  2011-06-08       Impact factor: 22.113

8.  Improved estimates of cancer-specific survival rates from population-based data.

Authors:  Nadia Howlader; Lynn A G Ries; Angela B Mariotto; Marsha E Reichman; Jennifer Ruhl; Kathleen A Cronin
Journal:  J Natl Cancer Inst       Date:  2010-10-11       Impact factor: 13.506

9.  Cumulative cause-specific mortality for cancer patients in the presence of other causes: a crude analogue of relative survival.

Authors:  K A Cronin; E J Feuer
Journal:  Stat Med       Date:  2000-07-15       Impact factor: 2.373

10.  Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models.

Authors:  Therese M L Andersson; Paul W Dickman; Sandra Eloranta; Paul C Lambert
Journal:  BMC Med Res Methodol       Date:  2011-06-22       Impact factor: 4.615

View more
  6 in total

1.  Impact of including second and later cancers in cause-specific survival estimates using population-based registry data.

Authors:  Gonçalo Forjaz; Nadia Howlader; Steve Scoppa; Christopher J Johnson; Angela B Mariotto
Journal:  Cancer       Date:  2021-10-08       Impact factor: 6.921

2.  Racial Differences in the Prognosis and Survival of Cutaneous Melanoma From 1990 to 2020 in North America: A Systematic Review and Meta-Analysis.

Authors:  Megan Lam; Jie Wei Zhu; Angie Hu; Jennifer Beecker
Journal:  J Cutan Med Surg       Date:  2021-10-22       Impact factor: 2.092

3.  Survival of Breast Cancer by Stage, Grade and Molecular Groups in Mallorca, Spain.

Authors:  Maria Clara Pascual; Juan José Montaño; Paula Franch; Carmen Sánchez-Contador; Maria Ramos
Journal:  J Clin Med       Date:  2022-09-27       Impact factor: 4.964

4.  Estimating Population-Based Recurrence Rates of Colorectal Cancer over Time in the United States.

Authors:  Natalia Kunst; Fernando Alarid-Escudero; Eline Aas; Veerle M H Coupé; Deborah Schrag; Karen M Kuntz
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2020-09-30       Impact factor: 4.254

5.  Epidemiology and Survival Outcomes of Lung Cancer: A Population-Based Study.

Authors:  Huan-Tang Lin; Fu-Chao Liu; Ching-Yang Wu; Chang-Fu Kuo; Wen-Ching Lan; Huang-Ping Yu
Journal:  Biomed Res Int       Date:  2019-12-28       Impact factor: 3.411

6.  Cancer outcomes research-a European challenge: measures of the cancer burden.

Authors:  Mette Kalager; Hans-Olov Adami; Pernilla Lagergren; Karen Steindorf; Paul W Dickman
Journal:  Mol Oncol       Date:  2021-06-22       Impact factor: 6.603

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.