Literature DB >> 9682323

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

C Robertson1, P Boyle.   

Abstract

In a companion article we have reviewed a number of available modelling approaches employed in estimating the influence of age, period and cohort effects on chronic disease rates. Here we review some of the graphical methods for displaying disease rates with a view to extracting information about the separate and joint effects of age, period and cohort. The more traditional displays such as line charts are compared to approaches based on smoothing and two- and three-dimensional plots which have recently been proposed. Other graphical techniques which are principally concerned with displaying interactions, such as biplots and correspondence analysis, are also considered. These techniques are illustrated with examples to compare the techniques revealing their strengths and weaknesses. It is clear that graphical approaches can be useful tools in understanding the behaviour of chronic disease time trends.

Mesh:

Year:  1998        PMID: 9682323     DOI: 10.1002/(sici)1097-0258(19980630)17:12<1325::aid-sim854>3.0.co;2-r

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


  10 in total

1.  Secular trends in adolescent never smoking from 1990 to 1999 in California: an age-period-cohort analysis.

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2.  Age, period, and cohort effects in motor vehicle mortality in the United States, 1980-2010: the role of sex, alcohol involvement, and position in vehicle.

Authors:  James Macinko; Diana Silver; Jin Yung Bae
Journal:  J Safety Res       Date:  2014-12-24

3.  Explaining Changes in the Patterns of Black Suicide in the United States From 1981 to 2002: An Age, Cohort, and Period Analysis.

Authors:  Sean Joe
Journal:  J Black Psychol       Date:  2006-08-01

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

Authors:  Philip S Rosenberg; William F Anderson
Journal:  Stat Med       Date:  2010-05-20       Impact factor: 2.373

7.  Age-related crossover in breast cancer incidence rates between black and white ethnic groups.

Authors:  William F Anderson; Philip S Rosenberg; Idan Menashe; Aya Mitani; Ruth M Pfeiffer
Journal:  J Natl Cancer Inst       Date:  2008-12-09       Impact factor: 13.506

8.  Changes in body mass index by birth cohort in Japanese adults: results from the National Nutrition Survey of Japan 1956-2005.

Authors:  Ikuko Funatogawa; Takashi Funatogawa; Mutsuhiro Nakao; Kanae Karita; Eiji Yano
Journal:  Int J Epidemiol       Date:  2008-09-09       Impact factor: 7.196

9.  Mortality cohort effects from mid-19th to mid-20th century Britain: did they exist?

Authors:  Yu-Kang Tu; Katherine Keyes; George Davey Smith
Journal:  Ann Epidemiol       Date:  2014-06-14       Impact factor: 3.797

10.  Comparison of annual percentage change in breast cancer incidence rate between Taiwan and the United States-A smoothed Lexis diagram approach.

Authors:  Li-Hsin Chien; Tzu-Jui Tseng; Chung-Hsing Chen; Hsin-Fang Jiang; Fang-Yu Tsai; Tsang-Wu Liu; Chao A Hsiung; I-Shou Chang
Journal:  Cancer Med       Date:  2017-05-31       Impact factor: 4.452

  10 in total

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