Literature DB >> 22569947

The Cancer Survival Query System: making survival estimates from the Surveillance, Epidemiology, and End Results program more timely and relevant for recently diagnosed patients.

Eric J Feuer1, Minjung Lee, Angela B Mariotto, Kathy A Cronin, Steve Scoppa, David F Penson, Mark Hachey, Laurie Cynkin, Ginger A Carter, David Campbell, Antoinette Percy-Laurry, Zhaohui Zou, Deborah Schrag, Benjamin F Hankey.   

Abstract

BACKGROUND: Population-based cancer registries that include patient follow-up generally provide information regarding net survival (ie, survival associated with the risk of dying of cancer in the absence of competing risks). However, registry data also can be used to calculate survival from cancer in the presence of competing risks, which is more clinically relevant.
METHODS: Statistical methods were developed to predict the risk of death from cancer and other causes, as well as natural life expectancy if the patient did not have cancer based on a profile of prognostic factors including characteristics of the cancer, demographic factors, and comorbid conditions. The Surveillance, Epidemiology, and End Results (SEER) Program database was used to calculate the risk of dying of cancer. Because the risks of dying of cancer versus other causes are assumed to be independent conditional on the prognostic factors, a wide variety of independent data sources can be used to calculate the risk of death from other causes. Herein, the risk of death from other causes was estimated using SEER and Medicare claims data, and was matched to the closest fitting portion of the US life table to obtain a "health status-adjusted age."
RESULTS: A nomogram was developed for prostate cancer as part of a Web-based Cancer Survival Query System that is targeted for use by physicians and patients to obtain information on a patient's prognosis. More nomograms currently are being developed.
CONCLUSIONS: Nomograms of this type can be used as one tool to assist cancer physicians and their patients to better understand their prognosis and to weigh alternative treatment and palliative strategies.
Copyright © 2012 American Cancer Society.

Entities:  

Mesh:

Year:  2012        PMID: 22569947     DOI: 10.1002/cncr.27615

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  17 in total

1.  Time trend of medical economic outcomes of endoscopic submucosal dissection for gastric cancer in Japan: a national database analysis.

Authors:  Atsuhiko Murata; Kohji Okamoto; Keiji Muramatsu; Shinya Matsuda
Journal:  Gastric Cancer       Date:  2013-06-26       Impact factor: 7.370

Review 2.  A systematic literature review of life expectancy prediction tools for patients with localized prostate cancer.

Authors:  Matthew Kent; Andrew J Vickers
Journal:  J Urol       Date:  2014-11-15       Impact factor: 7.450

Review 3.  Circumstance of endoscopic and laparoscopic treatments for gastric cancer in Japan: A review of epidemiological studies using a national administrative database.

Authors:  Atsuhiko Murata; Shinya Matsuda
Journal:  World J Gastrointest Endosc       Date:  2015-02-16

4.  Health-care utilization by prognosis profile in a managed care setting: using the Surveillance, Epidemiology and End Results Cancer Survival Calculator SEER*CSC.

Authors:  Borsika A Rabin; Jennifer L Ellis; John F Steiner; Larissa Nekhlyudov; Eric J Feuer; Benjamin F Hankey; Laurie Cynkin; Elizabeth Bayliss
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

5.  Cancer survival: an overview of measures, uses, and interpretation.

Authors:  Angela B Mariotto; Anne-Michelle Noone; Nadia Howlader; Hyunsoon Cho; Gretchen E Keel; Jessica Garshell; Steven Woloshin; Lisa M Schwartz
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

6.  Providing clinicians and patients with actual prognosis: cancer in the context of competing causes of death.

Authors:  Nadia Howlader; Angela B Mariotto; Steven Woloshin; Lisa M Schwartz
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

7.  The Surveillance, Epidemiology, and End Results Cancer Survival Calculator SEER*CSC: validation in a managed care setting.

Authors:  Eric J Feuer; Borsika A Rabin; Zhaohui Zou; Zhuoqiao Wang; Xiaoqin Xiong; Jennifer L Ellis; John F Steiner; Laurie Cynkin; Larissa Nekhlyudov; Elizabeth Bayliss; Benjamin F Hankey
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

8.  Multimorbidity: Implications and directions for health psychology and behavioral medicine.

Authors:  Jerry Suls; Paige A Green; Cynthia M Boyd
Journal:  Health Psychol       Date:  2019-09       Impact factor: 4.267

9.  Annual Report to the Nation on the status of cancer, 1975-2010, featuring prevalence of comorbidity and impact on survival among persons with lung, colorectal, breast, or prostate cancer.

Authors:  Brenda K Edwards; Anne-Michelle Noone; Angela B Mariotto; Edgar P Simard; Francis P Boscoe; S Jane Henley; Ahmedin Jemal; Hyunsoon Cho; Robert N Anderson; Betsy A Kohler; Christie R Eheman; Elizabeth M Ward
Journal:  Cancer       Date:  2013-12-16       Impact factor: 6.860

10.  Life tables adjusted for comorbidity more accurately estimate noncancer survival for recently diagnosed cancer patients.

Authors:  Angela B Mariotto; Zhuoqiao Wang; Carrie N Klabunde; Hyunsoon Cho; Barnali Das; Eric J Feuer
Journal:  J Clin Epidemiol       Date:  2013-09-10       Impact factor: 6.437

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