Literature DB >> 20209480

Proportional hazards models and age-period-cohort analysis of cancer rates.

Philip S Rosenberg1, William F Anderson.   

Abstract

Age-period-cohort (APC) analysis is widely used in cancer epidemiology to model trends in cancer rates. We develop methods for comparative APC analysis of two independent cause-specific hazard rates assuming that an APC model holds for each one. We construct linear hypothesis tests to determine whether the two hazards are absolutely proportional or proportional after stratification by cohort, period, or age. When a given proportional hazards model appears adequate, we derive simple expressions for the relative hazards using identifiable APC parameters. To demonstrate the utility of these new methods, we analyze cancer incidence rates in the United States in blacks versus whites for selected cancers, using data from the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. The examples illustrate that each type of proportionality may be encountered in practice. Published in 2010 by John Wiley & Sons, Ltd.

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Year:  2010        PMID: 20209480      PMCID: PMC2904510          DOI: 10.1002/sim.3865

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


  23 in total

1.  Increase in testicular cancer incidence in six European countries: a birth cohort phenomenon.

Authors:  R Bergström; H O Adami; M Möhner; W Zatonski; H Storm; A Ekbom; S Tretli; L Teppo; O Akre; T Hakulinen
Journal:  J Natl Cancer Inst       Date:  1996-06-05       Impact factor: 13.506

Review 2.  Age-period-cohort models of chronic disease rates. II: Graphical approaches.

Authors:  C Robertson; P Boyle
Journal:  Stat Med       Date:  1998-06-30       Impact factor: 2.373

Review 3.  Age-period-cohort analysis of chronic disease rates. I: Modelling approach.

Authors:  C Robertson; P Boyle
Journal:  Stat Med       Date:  1998-06-30       Impact factor: 2.373

4.  Modeling of time trends and interactions in vital rates using restricted regression splines.

Authors:  C Heuer
Journal:  Biometrics       Date:  1997-03       Impact factor: 2.571

5.  Models for temporal variation in cancer rates. I: Age-period and age-cohort models.

Authors:  D Clayton; E Schifflers
Journal:  Stat Med       Date:  1987-06       Impact factor: 2.373

6.  Models for temporal variation in cancer rates. II: Age-period-cohort models.

Authors:  D Clayton; E Schifflers
Journal:  Stat Med       Date:  1987-06       Impact factor: 2.373

7.  Breast cancer trends of black women compared with white women.

Authors:  K C Chu; R E Tarone; O W Brawley
Journal:  Arch Fam Med       Date:  1999 Nov-Dec

8.  Evaluation of birth cohort patterns in population disease rates.

Authors:  R E Tarone; K C Chu
Journal:  Am J Epidemiol       Date:  1996-01-01       Impact factor: 4.897

9.  Graphical presentation of trends in rates.

Authors:  S S Devesa; J Donaldson; T Fears
Journal:  Am J Epidemiol       Date:  1995-02-15       Impact factor: 4.897

10.  Etiologic heterogeneity for cervical carcinoma by histopathologic type, using comparative age-period-cohort models.

Authors:  Laura L Reimers; William F Anderson; Philip S Rosenberg; Donald E Henson; Philip E Castle
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-03-03       Impact factor: 4.254

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  18 in total

1.  Non-Hodgkin lymphoma in early life.

Authors:  Benjamin Emmanuel; William F Anderson
Journal:  J Natl Cancer Inst       Date:  2012-05-22       Impact factor: 13.506

2.  Estrogen Receptor Status and the Future Burden of Invasive and In Situ Breast Cancers in the United States.

Authors:  Philip S Rosenberg; Kimberly A Barker; William F Anderson
Journal:  J Natl Cancer Inst       Date:  2015-06-10       Impact factor: 13.506

3.  Age-specific trends in incidence of noncardia gastric cancer in US adults.

Authors:  William F Anderson; M Constanza Camargo; Joseph F Fraumeni; Pelayo Correa; Philip S Rosenberg; Charles S Rabkin
Journal:  JAMA       Date:  2010-05-05       Impact factor: 56.272

4.  A web tool for age-period-cohort analysis of cancer incidence and mortality rates.

Authors:  Philip S Rosenberg; David P Check; William F Anderson
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-08-21       Impact factor: 4.254

5.  Age-period-cohort models in cancer surveillance research: ready for prime time?

Authors:  Philip S Rosenberg; William F Anderson
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2011-05-24       Impact factor: 4.254

6.  Past, Current, and Future Incidence Rates and Burden of Metastatic Prostate Cancer in the United States.

Authors:  Scott P Kelly; William F Anderson; Philip S Rosenberg; Michael B Cook
Journal:  Eur Urol Focus       Date:  2017-11-20

Review 7.  How many etiological subtypes of breast cancer: two, three, four, or more?

Authors:  William F Anderson; Philip S Rosenberg; Aleix Prat; Charles M Perou; Mark E Sherman
Journal:  J Natl Cancer Inst       Date:  2014-08-12       Impact factor: 13.506

8.  Increasing risk of uterine cervical cancer among young Japanese women: Comparison of incidence trends in Japan, South Korea and Japanese-Americans between 1985 and 2012.

Authors:  Mai Utada; Pavel Chernyavskiy; Won Jin Lee; Silvia Franceschi; Catherine Sauvaget; Amy Berrington de Gonzalez; Diana R Withrow
Journal:  Int J Cancer       Date:  2018-12-18       Impact factor: 7.396

9.  Pediatric, elderly, and emerging adult-onset peaks in Burkitt's lymphoma incidence diagnosed in four continents, excluding Africa.

Authors:  Sam M Mbulaiteye; William F Anderson; Jacques Ferlay; Kishor Bhatia; Cindy Chang; Philip S Rosenberg; Susan S Devesa; Donald M Parkin
Journal:  Am J Hematol       Date:  2012-04-10       Impact factor: 10.047

10.  Correlated Poisson models for age-period-cohort analysis.

Authors:  Pavel Chernyavskiy; Mark P Little; Philip S Rosenberg
Journal:  Stat Med       Date:  2017-10-04       Impact factor: 2.373

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