Literature DB >> 7766773

Methods of inference for estimates of absolute risk derived from population-based case-control studies.

J Benichou1, M H Gail.   

Abstract

The absolute risk is the probability of developing a given disease over a specified time interval given age and risk factors. Gail, et al. (1989, Journal of the National Cancer Institute 81, 1879-1888) obtained point estimates from population-based case control data by combining relative risk estimates from the case control data and composite incidence estimates from the cohort data. They also obtained variance estimates, but they only took into account the variability in estimating relative risks. In this paper, we present variance estimates that take into account all components of variability, namely the variance of relative risk estimates and of baseline incidence estimates, as well as the covariance between the two, the latter term being obtained by using implicit delta method arguments (Benichou and Gail, 1989, The American Statistician 43, 41-44). Simulations demonstrate the validity of such variance estimates as well as of corresponding confidence intervals. These methods are applied to a population-based case control study of breast cancer.

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Year:  1995        PMID: 7766773

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  10 in total

1.  Bridging the etiologic and prognostic outlooks in individualized assessment of absolute risk of an illness: application in lung cancer.

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Journal:  Eur J Epidemiol       Date:  2016-07-11       Impact factor: 8.082

2.  Genetic and clinical predictors for breast cancer risk assessment and stratification among Chinese women.

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Journal:  J Natl Cancer Inst       Date:  2010-05-18       Impact factor: 13.506

3.  On estimation of time-dependent attributable fraction from population-based case-control studies.

Authors:  Wei Zhao; Ying Qing Chen; Li Hsu
Journal:  Biometrics       Date:  2017-01-18       Impact factor: 2.571

4.  Variance computations for functional of absolute risk estimates.

Authors:  R M Pfeiffer; E Petracci
Journal:  Stat Probab Lett       Date:  2011-07-01       Impact factor: 0.870

5.  Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

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Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2018-04-20       Impact factor: 4.254

6.  Population-based absolute risk estimation with survey data.

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Review 7.  Risk models used to counsel women for breast and ovarian cancer: a guide for clinicians.

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Journal:  Fam Cancer       Date:  2001       Impact factor: 2.375

8.  Assessing the value of risk predictions by using risk stratification tables.

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Journal:  Ann Intern Med       Date:  2008-11-18       Impact factor: 25.391

9.  A regression model for risk difference estimation in population-based case-control studies clarifies gender differences in lung cancer risk of smokers and never smokers.

Authors:  Stephanie A Kovalchik; Sara De Matteis; Maria Teresa Landi; Neil E Caporaso; Ravi Varadhan; Dario Consonni; Andrew W Bergen; Hormuzd A Katki; Sholom Wacholder
Journal:  BMC Med Res Methodol       Date:  2013-11-19       Impact factor: 4.615

10.  Predicting Adverse Health Outcomes in Long-Term Survivors of a Childhood Cancer.

Authors:  Chaya S Moskowitz; Kevin C Oeffinger
Journal:  Children (Basel)       Date:  2014-09
  10 in total

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