{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:18:23Z","timestamp":1787505503331,"version":"build-2736575974"},"reference-count":25,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2005,12,1]],"date-time":"2005-12-01T00:00:00Z","timestamp":1133395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Geriatr Psychiatry Neurol"],"published-print":{"date-parts":[[2005,12]]},"abstract":"<jats:p>This article explores new statistical methodologies for using longitudinal data in the early prediction of Alzheimer\u2019s disease (AD). Specifically, the authors examine some new techniques that allow the joint or \u201cshared\u201d estimation of longitudinal components based on both duration (survival) and quantitative changes (growth curves). These new shared growth-survival parameter models may be used to characterize the declining functions that anticipate the onset of AD. The authors apply these models to data from the Kungsholmen Project, a longitudinal study of aging in Stockholm, Sweden. They examine age-based survival-frailty models for the onset of AD, latent growth-decline curve models for changes in cognition over age, and 3 alternative forms of models for the shared relationships of survival and early cognitive decline. The accuracy and reliability of this approach is considered for a better understanding of the developmental course of AD in these data, including the potential removal of biases due to subject selection.<\/jats:p>","DOI":"10.1177\/0891988705281879","type":"journal-article","created":{"date-parts":[[2005,11,23]],"date-time":"2005-11-23T12:54:12Z","timestamp":1132750452000},"page":"234-241","source":"Crossref","is-referenced-by-count":40,"title":["Longitudinal Models of Growth and Survival Applied to the Early Detection of Alzheimer\u2019s Disease"],"prefix":"10.1177","volume":"18","author":[{"given":"John J.","family":"McArdle","sequence":"first","affiliation":[{"name":"Department of Psychology, University of Southern California, Los Angeles,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brent J.","family":"Small","sequence":"additional","affiliation":[{"name":"School of Aging Studies, University of South Florida, 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