{"doi":"10.1002/trc2.12041","title":"Comparison of Cox proportional hazards regression and generalized Cox regression models applied in dementia risk prediction","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction</jats:title>\n                    <jats:p>The frequently used Cox regression applies two critical assumptions, which might not hold for all predictors. In this study, the results from a Cox regression model (CM) and a generalized Cox regression model (GCM) are compared.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>Data are from the Survey of Health, Ageing and Retirement in Europe (SHARE), which includes approximately 140,000 individuals aged 50 or older followed over seven waves. CMs and GCMs are used to estimate dementia risk. The results are internally and externally validated.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>None of the predictors included in the analyses fulfilled the assumptions of Cox regression. Both models predict dementia moderately well (10‐year risk: 0.737; 95% confidence interval [CI]: 0.699, 0.773; CM and 0.746; 95% CI: 0.710, 0.785; GCM).</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion</jats:title>\n                    <jats:p>\n                      The GCM performs significantly better than the CM when comparing pseudo‐R\n                      <jats:sup>2</jats:sup>\n                      and the log‐likelihood. GCMs enable researcher to test the assumptions used by Cox regression independently and relax these assumptions if necessary.\n                    </jats:p>\n                  </jats:sec>","journal":"Alzheimer's &amp; Dementia: Translational Research &amp; Clinical Interventions","year":2020,"id":593370,"datarank":0.4566783656585135,"base_score":3.044522437723423,"endowment":3.044522437723423,"self_citation_contribution":0.4566783656585135,"citation_network_contribution":0.0,"self_endowment_contribution":0.4566783656585135,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":20,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":251480,"name":"Isabelle Carrière","orcid":"0000-0002-3617-0752","position":1,"is_corresponding":false},{"id":889983,"name":"Graciela Muniz‐Terrera","orcid":null,"position":2,"is_corresponding":false},{"id":585524,"name":"Jantje Goerdten","orcid":"0000-0002-7814-1802","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Comparison of Cox proportional hazards regression and generalized Cox regression models applied in dementia risk prediction","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction</jats:title>\n                    <jats:p>The frequently used Cox regression applies two critical assumptions, which might not hold for all predictors. In this study, the results from a Cox regression model (CM) and a generalized Cox regression model (GCM) are compared.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>Data are from the Survey of Health, Ageing and Retirement in Europe (SHARE), which includes approximately 140,000 individuals aged 50 or older followed over seven waves. CMs and GCMs are used to estimate dementia risk. The results are internally and externally validated.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>None of the predictors included in the analyses fulfilled the assumptions of Cox regression. Both models predict dementia moderately well (10‐year risk: 0.737; 95% confidence interval [CI]: 0.699, 0.773; CM and 0.746; 95% CI: 0.710, 0.785; GCM).</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion</jats:title>\n                    <jats:p>\n                      The GCM performs significantly better than the CM when comparing pseudo‐R\n                      <jats:sup>2</jats:sup>\n                      and the log‐likelihood. GCMs enable researcher to test the assumptions used by Cox regression independently and relax these assumptions if necessary.\n                    </jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"32548239","pmcid":"PMC7293996","openalex_id":"https://openalex.org/W32548239","authors":[],"funders":[{"funder_name":"European Commission","grant_id":"QLK6‐CT‐2001‐00360","title":null},{"funder_name":"European Commission","grant_id":"CIT5‐CT‐2005‐028857","title":null},{"funder_name":"European Commission","grant_id":"GA N°261982","title":null},{"funder_name":"National Institute on Aging","grant_id":"U01_AG09740‐13S2","title":null},{"funder_name":"National Institute on Aging","grant_id":"P01_AG005842","title":null},{"funder_name":"National Institute on Aging","grant_id":"P01_AG08291","title":null},{"funder_name":"National Institute on Aging","grant_id":"P30_AG12815","title":null},{"funder_name":"National Institute on Aging","grant_id":"R21_AG025169","title":null},{"funder_name":"National Institute on Aging","grant_id":"Y1‐AG‐4553‐01","title":null},{"funder_name":"National Institute on Aging","grant_id":"IAG_BSR06‐11","title":null},{"funder_name":"National Institute on Aging","grant_id":"HHSN271201300071C","title":null},{"funder_name":"NIA NIH HHS","grant_id":"U01 AG009740","title":null},{"funder_name":"National Institutes of Health","grant_id":"3U01AG009740-24S2","title":"Health and Retirement Study Yrs 23-28"},{"funder_name":"National Institutes of Health","grant_id":"5P01AG008291-19","title":"HEALTH AND ECONOMIC STATUS IN OLDER POPULATIONS"},{"funder_name":"European Commission","grant_id":"654221","title":"Synergies for Europe's Research Infrastructures in the Social Sciences"},{"funder_name":"European Commission","grant_id":"211909","title":"Upgrading the Survey of Health, Ageing and Retirement in Europe – preparatory phase"},{"funder_name":"European Commission","grant_id":"227822","title":"Longitudinal Enhancement and Access imProvement of the SHARE infrastructure"},{"funder_name":"National Institutes of Health","grant_id":"5P30AG012815-10","title":"RAND CENTER FOR THE STUDY OF AGING"},{"funder_name":"European Commission","grant_id":"676536","title":"Achieving world-class standards in all SHARE countries"},{"funder_name":"European Commission","grant_id":"261982","title":"Multinational Advancement of Research Infrastructures on Ageing"},{"funder_name":"National Institutes of Health","grant_id":"1R21AG025169-01","title":"DEVELOPING AN ISRAELI VERSION OF THE HRS/SHARE PROJECT"}],"total_grants":21,"fwci":0.0,"citation_percentile":0.00641145,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by-nc-nd","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/trc2.12041","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/trc2.12041","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1002/trc2.12041","host_type":"publisher"},{"url":"https://alz-journals.onlinelibrary.wiley.com/doi/pdf/10.1002/trc2.12041","host_type":"publisher"},{"url":"https://dialnet.unirioja.es/servlet/articulo?codigo=2871470","host_type":"journal"},{"url":"https://www.research.ed.ac.uk/en/publications/9a7a99d0-4b10-4be7-980f-e8c51673ac9a","host_type":"repository"},{"url":"https://doaj.org/article/54b5e59d4fe7475e92473c414aeb9efa","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7293996","host_type":"repository"},{"url":"https://onlinelibrary.wiley.com/doi/abs/10.1002/trc2.12041","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC7293996","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC7293996?pdf=render","host_type":"Europe_PMC"},{"url":"https://doi.org/10.1002/trc2.12041","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/32548239","host_type":""},{"url":"http://dx.doi.org/10.1002/trc2.12041","host_type":""},{"url":"https://dx.doi.org/10.1002/trc2.12041","host_type":""},{"url":"https://hdl.handle.net/20.500.11820/9a7a99d0-4b10-4be7-980f-e8c51673ac9a","host_type":""},{"url":"https://www.pure.ed.ac.uk/ws/files/160061890/trc2.12041.pdf","host_type":""}],"fields_of_study":["Political and Social Issues","French Urban and Social Studies","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":[],"keywords":["Economics","Prediction","Dementia","Splines","Cox Proportional Hazards Regression","Dementia Risk Model","Geriatrics","RC952-954.6","Neurology. 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