Literature DB >> 24242701

Competing risks analysis in mortality estimation for breast cancer patients from independent risk groups.

Shengfan Zhang1, Julie S Ivy, James R Wilson, Kathleen M Diehl, Bonnie C Yankaskas.   

Abstract

This study quantifies breast cancer mortality in the presence of competing risks for complex patients. Breast cancer behaves differently in different patient populations, which can have significant implications for patient survival; hence these differences must be considered when making screening and treatment decisions. Mortality estimation for breast cancer patients has been a significant research question. Accurate estimation is critical for clinical decision making, including recommendations. In this study, a competing risks framework is built to analyze the effect of patient risk factors and cancer characteristics on breast cancer and other cause mortality. To estimate mortality probabilities from breast cancer and other causes as a function of not only the patient's age or race but also biomarkers for estrogen and progesterone receptor status, a nonparametric cumulative incidence function is formulated using data from the community-based Carolina Mammography Registry. Based on the log(-log) transformation, confidence intervals are constructed for mortality estimates over time. To compare mortality probabilities in two independent risk groups at a given time, a method with improved power is formulated using the log(-log) transformation.

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Year:  2013        PMID: 24242701     DOI: 10.1007/s10729-013-9255-x

Source DB:  PubMed          Journal:  Health Care Manag Sci        ISSN: 1386-9620


  20 in total

Review 1.  Estimation of failure probabilities in the presence of competing risks: new representations of old estimators.

Authors:  T A Gooley; W Leisenring; J Crowley; B E Storer
Journal:  Stat Med       Date:  1999-03-30       Impact factor: 2.373

2.  Non-parametric confidence interval estimation for competing risks analysis: application to contraceptive data.

Authors:  Jahar B Choudhury
Journal:  Stat Med       Date:  2002-04-30       Impact factor: 2.373

3.  Effect of baseline breast density on breast cancer incidence, stage, mortality, and screening parameters: 25-year follow-up of a Swedish mammographic screening.

Authors:  Sherry Yueh-Hsia Chiu; Stephen Duffy; Amy Ming-Fang Yen; Laszlo Tabár; Robert A Smith; Hsiu-Hsi Chen
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-04-20       Impact factor: 4.254

Review 4.  Global patterns of cancer incidence and mortality rates and trends.

Authors:  Ahmedin Jemal; Melissa M Center; Carol DeSantis; Elizabeth M Ward
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-07-20       Impact factor: 4.254

5.  The association of breast density with breast cancer mortality in African American and white women screened in community practice.

Authors:  Shengfan Zhang; Julie S Ivy; Kathleen M Diehl; Bonnie C Yankaskas
Journal:  Breast Cancer Res Treat       Date:  2012-11-10       Impact factor: 4.872

6.  The analysis of failure times in the presence of competing risks.

Authors:  R L Prentice; J D Kalbfleisch; A V Peterson; N Flournoy; V T Farewell; N E Breslow
Journal:  Biometrics       Date:  1978-12       Impact factor: 2.571

7.  Breast cancer mortality trends in the United States according to estrogen receptor status and age at diagnosis.

Authors:  Ismail Jatoi; Bingshu E Chen; William F Anderson; Philip S Rosenberg
Journal:  J Clin Oncol       Date:  2007-04-02       Impact factor: 44.544

8.  A study in causal discovery from population-based infant birth and death records.

Authors:  S Mani; G F Cooper
Journal:  Proc AMIA Symp       Date:  1999

9.  Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer.

Authors:  Donald A Berry; Constance Cirrincione; I Craig Henderson; Marc L Citron; Daniel R Budman; Lori J Goldstein; Silvana Martino; Edith A Perez; Hyman B Muss; Larry Norton; Clifford Hudis; Eric P Winer
Journal:  JAMA       Date:  2006-04-12       Impact factor: 56.272

10.  A method for partitioning cancer mortality trends by factors associated with diagnosis: an application to female breast cancer.

Authors:  K C Chu; B A Miller; E J Feuer; B F Hankey
Journal:  J Clin Epidemiol       Date:  1994-12       Impact factor: 6.437

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  1 in total

1.  Dynamic Risk Prediction via a Joint Frailty-Copula Model and IPD Meta-Analysis: Building Web Applications.

Authors:  Takeshi Emura; Hirofumi Michimae; Shigeyuki Matsui
Journal:  Entropy (Basel)       Date:  2022-04-22       Impact factor: 2.738

  1 in total

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