Literature DB >> 18058231

Estimation and interpretation of models of absolute risk from epidemiologic data, including family-based studies.

Mitchell H Gail1.   

Abstract

Absolute risk is the chance that a person with given risk factors and free of the disease of interest at age a will be diagnosed with that disease in the interval (a, a + tau]. Absolute risk is sometimes called cumulative incidence. Absolute risk is a "crude" risk because it is reduced by the chance that the person will die of competing causes of death before developing the disease of interest. Cohort studies admit flexibility in modeling absolute risk, either by allowing covariates to affect the cause-specific relative hazards or to affect the absolute risk itself. An advantage of cause-specific relative risk models is that various data sources can be used to fit the required components. For example, case-control data can be used to estimate relative risk and attributable risk, and these can be combined with registry data on age-specific composite hazard rates for the disease of interest and with national data on competing hazards of mortality to estimate absolute risk. Family-based designs, such as the kin-cohort design and collections of pedigrees with multiple affected individuals can be used to estimate the genotype-specific hazard of disease. Such analyses must be adjusted for ascertainment, and failure to take into account residual familial risk, such as might be induced by unmeasured genetic variants or by unmeasured behavioral or environmental exposures that are correlated within families, can lead to overestimates of mutation-specific absolute risk in the general population.

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Year:  2007        PMID: 18058231     DOI: 10.1007/s10985-007-9070-0

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  39 in total

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2.  Bias and efficiency in family-based gene-characterization studies: conditional, prospective, retrospective, and joint likelihoods.

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4.  Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history.

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5.  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

6.  Risk-based recommendations for mammographic screening for women in their forties.

Authors:  M Gail; B Rimer
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7.  Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. The Breast Cancer Linkage Consortium.

Authors:  D Ford; D F Easton; M Stratton; S Narod; D Goldgar; P Devilee; D T Bishop; B Weber; G Lenoir; J Chang-Claude; H Sobol; M D Teare; J Struewing; A Arason; S Scherneck; J Peto; T R Rebbeck; P Tonin; S Neuhausen; R Barkardottir; J Eyfjord; H Lynch; B A Ponder; S A Gayther; M Zelada-Hedman
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8.  Breast and ovarian cancer incidence in BRCA1-mutation carriers. Breast Cancer Linkage Consortium.

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9.  On the use of familial aggregation in population-based case probands for calculating penetrance.

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10.  Genome-wide association study identifies novel breast cancer susceptibility loci.

Authors:  Douglas F Easton; Karen A Pooley; Alison M Dunning; Paul D P Pharoah; Deborah Thompson; Dennis G Ballinger; Jeffery P Struewing; Jonathan Morrison; Helen Field; Robert Luben; Nicholas Wareham; Shahana Ahmed; Catherine S Healey; Richard Bowman; Kerstin B Meyer; Christopher A Haiman; Laurence K Kolonel; Brian E Henderson; Loic Le Marchand; Paul Brennan; Suleeporn Sangrajrang; Valerie Gaborieau; Fabrice Odefrey; Chen-Yang Shen; Pei-Ei Wu; Hui-Chun Wang; Diana Eccles; D Gareth Evans; Julian Peto; Olivia Fletcher; Nichola Johnson; Sheila Seal; Michael R Stratton; Nazneen Rahman; Georgia Chenevix-Trench; Stig E Bojesen; Børge G Nordestgaard; Christen K Axelsson; Montserrat Garcia-Closas; Louise Brinton; Stephen Chanock; Jolanta Lissowska; Beata Peplonska; Heli Nevanlinna; Rainer Fagerholm; Hannaleena Eerola; Daehee Kang; Keun-Young Yoo; Dong-Young Noh; Sei-Hyun Ahn; David J Hunter; Susan E Hankinson; David G Cox; Per Hall; Sara Wedren; Jianjun Liu; Yen-Ling Low; Natalia Bogdanova; Peter Schürmann; Thilo Dörk; Rob A E M Tollenaar; Catharina E Jacobi; Peter Devilee; Jan G M Klijn; Alice J Sigurdson; Michele M Doody; Bruce H Alexander; Jinghui Zhang; Angela Cox; Ian W Brock; Gordon MacPherson; Malcolm W R Reed; Fergus J Couch; Ellen L Goode; Janet E Olson; Hanne Meijers-Heijboer; Ans van den Ouweland; André Uitterlinden; Fernando Rivadeneira; Roger L Milne; Gloria Ribas; Anna Gonzalez-Neira; Javier Benitez; John L Hopper; Margaret McCredie; Melissa Southey; Graham G Giles; Chris Schroen; Christina Justenhoven; Hiltrud Brauch; Ute Hamann; Yon-Dschun Ko; Amanda B Spurdle; Jonathan Beesley; Xiaoqing Chen; Arto Mannermaa; Veli-Matti Kosma; Vesa Kataja; Jaana Hartikainen; Nicholas E Day; David R Cox; Bruce A J Ponder
Journal:  Nature       Date:  2007-06-28       Impact factor: 49.962

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Journal:  J Clin Oncol       Date:  2012-11-19       Impact factor: 44.544

2.  Using lifetime risk estimates in personal genomic profiles: estimation of uncertainty.

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3.  Estimation of genotype relative risks from pedigree data by retrospective likelihoods.

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  3 in total

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