Literature DB >> 22170169

Predicting the absolute risk of dying from colorectal cancer and from other causes using population-based cancer registry data.

Minjung Lee1, Kathleen A Cronin, Mitchell H Gail, Eric J Feuer.   

Abstract

This paper describes how population cancer registry data from the Surveillance, Epidemiology, and End Results program of the National Cancer Institute can be used to develop a prognostic model to predict the absolute risk of mortality from cancer and from other causes for an individual with specific covariates. It incorporates previously developed methods for competing risk modeling along with an imputation method to address missing cause of death information. We illustrate these approaches with colorectal cancer and evaluate the model discriminatory and calibration accuracy by time-dependent area under the receiver operating characteristic curve and calibration plot.
Copyright © 2011 John Wiley & Sons, Ltd.

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Year:  2011        PMID: 22170169     DOI: 10.1002/sim.4454

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  7 in total

1.  Overview of model validation for survival regression model with competing risks using melanoma study data.

Authors:  Zhongheng Zhang; Giuliana Cortese; Christophe Combescure; Roger Marshall; Minjung Lee; Hyun Ja Lim; Bernhard Haller
Journal:  Ann Transl Med       Date:  2018-08

2.  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

3.  Prediction models for the risk of total knee replacement: development and validation using data from multicentre cohort studies.

Authors:  Qiang Liu; Hongling Chu; Michael P LaValley; David J Hunter; Hua Zhang; Liyuan Tao; Siyan Zhan; Jianhao Lin; Yuqing Zhang
Journal:  Lancet Rheumatol       Date:  2022-01-05

4.  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

5.  Increased risk of secondary lung cancer in patients with tuberculosis: A nationwide, population-based cohort study.

Authors:  Li-Ju Ho; Hung-Yi Yang; Chi-Hsiang Chung; Wei-Chin Chang; Sung-Sen Yang; Chien-An Sun; Wu-Chien Chien; Ruei-Yu Su
Journal:  PLoS One       Date:  2021-05-07       Impact factor: 3.240

6.  Impact of Comorbidities on Survival in Gastric, Colorectal, and Lung Cancer Patients.

Authors:  Toshitaka Morishima; Yoshifumi Matsumoto; Nobuyuki Koeda; Hiroko Shimada; Tsutomu Maruhama; Daisaku Matsuki; Kayo Nakata; Yuri Ito; Takahiro Tabuchi; Isao Miyashiro
Journal:  J Epidemiol       Date:  2018-07-14       Impact factor: 3.211

7.  A competing risk nomogram predicting cause-specific mortality in patients with lung adenosquamous carcinoma.

Authors:  Xiao Wu; Wenfeng Yu; R H Petersen; Hongxu Sheng; Yiqing Wang; Wang Lv; Jian Hu
Journal:  BMC Cancer       Date:  2020-05-16       Impact factor: 4.430

  7 in total

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