Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes
Age-period-cohort analysis of incidence and/or mortality data has received much attention in the literature. To circumvent the non-identifiability problem inherent in the age-period-cohort model, additional constraints are necessary on the parameters estimates. We propose setting the constraint to r...
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| description | Age-period-cohort analysis of incidence and/or mortality data has received much attention in the literature. To circumvent the non-identifiability problem inherent in the age-period-cohort model, additional constraints are necessary on the parameters estimates. We propose setting the constraint to reflect the different nature of the three temporal variables: age, period, and birth cohort. There are two assumptions in our method. Recognizing age effects to be deterministic (first assumption), we do not explicitly incorporate the age parameters into constraint. For the stochastic period and cohort effects, we set a constant-relative-variation constraint on their trends (second assumption). The constant-relative-variation constraint dictates that between two stochastic effects, one with a larger curvature gets a larger (absolute) slope, and one with zero curvature gets no slope. We conducted Monte-Carlo simulations to examine the statistical properties of the proposed method and analyzed the data of prostate cancer incidence for whites from 1973-2012 to illustrate the methodology. A driver for the period and/or cohort effect may be lacking in some populations. In that case, the CRV method automatically produces an unbiased age effect and no period and/or cohort effect, thereby addressing the situation properly. However, the method proposed in this paper is not a general purpose model and will produce biased results in many other real-life data scenarios. It is only useful in situations when the age effects are deterministic and dominant, and the period and cohort effects are stochastic and minor. |
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To circumvent the non-identifiability problem inherent in the age-period-cohort model, additional constraints are necessary on the parameters estimates. We propose setting the constraint to reflect the different nature of the three temporal variables: age, period, and birth cohort. There are two assumptions in our method. Recognizing age effects to be deterministic (first assumption), we do not explicitly incorporate the age parameters into constraint. For the stochastic period and cohort effects, we set a constant-relative-variation constraint on their trends (second assumption). The constant-relative-variation constraint dictates that between two stochastic effects, one with a larger curvature gets a larger (absolute) slope, and one with zero curvature gets no slope. We conducted Monte-Carlo simulations to examine the statistical properties of the proposed method and analyzed the data of prostate cancer incidence for whites from 1973-2012 to illustrate the methodology. A driver for the period and/or cohort effect may be lacking in some populations. In that case, the CRV method automatically produces an unbiased age effect and no period and/or cohort effect, thereby addressing the situation properly. However, the method proposed in this paper is not a general purpose model and will produce biased results in many other real-life data scenarios. It is only useful in situations when the age effects are deterministic and dominant, and the period and cohort effects are stochastic and minor.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0226678</identifier><identifier>PMID: 31856261</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Age Factors ; Analysis ; Bias ; Biology and Life Sciences ; Cancer ; Cohort analysis ; Cohort Studies ; Computer Simulation ; Constraint modelling ; Curvature ; Disease ; Epidemiology ; Health care ; Humans ; Incidence ; Male ; Medical technology ; Medicine and Health Sciences ; Monte Carlo Method ; Monte Carlo methods ; Monte Carlo simulation ; Mortality ; Parameter estimation ; People and Places ; Physical sciences ; Preventive medicine ; Prostate ; Prostate cancer ; Prostatic Neoplasms - epidemiology ; Public health ; Research and Analysis Methods ; Research Design - standards ; Slopes ; Stochasticity ; Survival Rate ; Taiwan ; Variation ; White People - statistics & numerical data</subject><ispartof>PloS one, 2019-12, Vol.14 (12), p.e0226678</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Su, Lee. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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To circumvent the non-identifiability problem inherent in the age-period-cohort model, additional constraints are necessary on the parameters estimates. We propose setting the constraint to reflect the different nature of the three temporal variables: age, period, and birth cohort. There are two assumptions in our method. Recognizing age effects to be deterministic (first assumption), we do not explicitly incorporate the age parameters into constraint. For the stochastic period and cohort effects, we set a constant-relative-variation constraint on their trends (second assumption). The constant-relative-variation constraint dictates that between two stochastic effects, one with a larger curvature gets a larger (absolute) slope, and one with zero curvature gets no slope. We conducted Monte-Carlo simulations to examine the statistical properties of the proposed method and analyzed the data of prostate cancer incidence for whites from 1973-2012 to illustrate the methodology. A driver for the period and/or cohort effect may be lacking in some populations. In that case, the CRV method automatically produces an unbiased age effect and no period and/or cohort effect, thereby addressing the situation properly. However, the method proposed in this paper is not a general purpose model and will produce biased results in many other real-life data scenarios. It is only useful in situations when the age effects are deterministic and dominant, and the period and cohort effects are stochastic and minor.</description><subject>Age</subject><subject>Age Factors</subject><subject>Analysis</subject><subject>Bias</subject><subject>Biology and Life Sciences</subject><subject>Cancer</subject><subject>Cohort analysis</subject><subject>Cohort Studies</subject><subject>Computer Simulation</subject><subject>Constraint modelling</subject><subject>Curvature</subject><subject>Disease</subject><subject>Epidemiology</subject><subject>Health care</subject><subject>Humans</subject><subject>Incidence</subject><subject>Male</subject><subject>Medical technology</subject><subject>Medicine and Health Sciences</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo methods</subject><subject>Monte Carlo simulation</subject><subject>Mortality</subject><subject>Parameter estimation</subject><subject>People and Places</subject><subject>Physical sciences</subject><subject>Preventive medicine</subject><subject>Prostate</subject><subject>Prostate cancer</subject><subject>Prostatic Neoplasms - epidemiology</subject><subject>Public health</subject><subject>Research and Analysis Methods</subject><subject>Research Design - standards</subject><subject>Slopes</subject><subject>Stochasticity</subject><subject>Survival Rate</subject><subject>Taiwan</subject><subject>Variation</subject><subject>White People - statistics & numerical data</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk02L2zAQhk1p6W63_QelNRRKe3BqSbZsXQph6UdgYaFfVzGRxomCYnklO23-fZWNd4nLHooPFjPP-45n5EmSlySfEVaRDxs3-BbsrHMtznJKOa_qR8k5EYxmnObs8cn5LHkWwibPS1Zz_jQ5Y6QuOeXkPNnPV5h16I3TmXJr5_sUous-mJD-Nv06hVS5NvTQ9plHC73ZYbYDb-LJtcecB9P2aeN8lKbQddEk5rYYg65Jj-YxpdOxQLCuw_A8edKADfhifF8kPz9_-nH5Nbu6_rK4nF9ligvaZ6UuKGvqquCkJHXTVIwXULNKURC1EEoAikIzUnGhiRCiYKAK5BSqPF_ikrCL5PXRt7MuyHFqQVJG64oISstILI6EdrCRnTdb8HvpwMjbgPMrCbElZVEuabPEWqsKgRREa9FogiUwjgWSsubR6-NYbVhuUas4BA92YjrNtGYtV24nY7O0oHU0eDcaeHczYOjl1gSF1kKLbrj9blExRgoW0Tf_oA93N1IriA2YtnGxrjqYyjnPD1VJTiM1e4CKj8atibeMjYnxieD9RBCZHv_0KxhCkIvv3_6fvf41Zd-esGsE26-Ds8PhlwpTsDiCyrsQPDb3Qya5PGzI3TTkYUPkuCFR9ur0gu5FdyvB_gICeg0C</recordid><startdate>20191219</startdate><enddate>20191219</enddate><creator>Su, Shih-Yung</creator><creator>Lee, Wen-Chung</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-3171-7672</orcidid></search><sort><creationdate>20191219</creationdate><title>Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes</title><author>Su, Shih-Yung ; Lee, Wen-Chung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-5d423f87461518ff7364a837c2a9899c9ae94d31769d199943ac4e62a700beb13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Age</topic><topic>Age Factors</topic><topic>Analysis</topic><topic>Bias</topic><topic>Biology and Life Sciences</topic><topic>Cancer</topic><topic>Cohort analysis</topic><topic>Cohort Studies</topic><topic>Computer Simulation</topic><topic>Constraint modelling</topic><topic>Curvature</topic><topic>Disease</topic><topic>Epidemiology</topic><topic>Health care</topic><topic>Humans</topic><topic>Incidence</topic><topic>Male</topic><topic>Medical technology</topic><topic>Medicine and Health Sciences</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo methods</topic><topic>Monte Carlo simulation</topic><topic>Mortality</topic><topic>Parameter estimation</topic><topic>People and Places</topic><topic>Physical sciences</topic><topic>Preventive medicine</topic><topic>Prostate</topic><topic>Prostate cancer</topic><topic>Prostatic Neoplasms - epidemiology</topic><topic>Public health</topic><topic>Research and Analysis Methods</topic><topic>Research Design - standards</topic><topic>Slopes</topic><topic>Stochasticity</topic><topic>Survival Rate</topic><topic>Taiwan</topic><topic>Variation</topic><topic>White People - statistics & numerical data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Su, Shih-Yung</creatorcontrib><creatorcontrib>Lee, Wen-Chung</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Su, Shih-Yung</au><au>Lee, Wen-Chung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-12-19</date><risdate>2019</risdate><volume>14</volume><issue>12</issue><spage>e0226678</spage><pages>e0226678-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Age-period-cohort analysis of incidence and/or mortality data has received much attention in the literature. To circumvent the non-identifiability problem inherent in the age-period-cohort model, additional constraints are necessary on the parameters estimates. We propose setting the constraint to reflect the different nature of the three temporal variables: age, period, and birth cohort. There are two assumptions in our method. Recognizing age effects to be deterministic (first assumption), we do not explicitly incorporate the age parameters into constraint. For the stochastic period and cohort effects, we set a constant-relative-variation constraint on their trends (second assumption). The constant-relative-variation constraint dictates that between two stochastic effects, one with a larger curvature gets a larger (absolute) slope, and one with zero curvature gets no slope. We conducted Monte-Carlo simulations to examine the statistical properties of the proposed method and analyzed the data of prostate cancer incidence for whites from 1973-2012 to illustrate the methodology. A driver for the period and/or cohort effect may be lacking in some populations. In that case, the CRV method automatically produces an unbiased age effect and no period and/or cohort effect, thereby addressing the situation properly. However, the method proposed in this paper is not a general purpose model and will produce biased results in many other real-life data scenarios. It is only useful in situations when the age effects are deterministic and dominant, and the period and cohort effects are stochastic and minor.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31856261</pmid><doi>10.1371/journal.pone.0226678</doi><tpages>e0226678</tpages><orcidid>https://orcid.org/0000-0003-3171-7672</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Age Age Factors Analysis Bias Biology and Life Sciences Cancer Cohort analysis Cohort Studies Computer Simulation Constraint modelling Curvature Disease Epidemiology Health care Humans Incidence Male Medical technology Medicine and Health Sciences Monte Carlo Method Monte Carlo methods Monte Carlo simulation Mortality Parameter estimation People and Places Physical sciences Preventive medicine Prostate Prostate cancer Prostatic Neoplasms - epidemiology Public health Research and Analysis Methods Research Design - standards Slopes Stochasticity Survival Rate Taiwan Variation White People - statistics & numerical data |
| title | Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes |
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