View Item 

Show simple item record

Associations between variability of risk factors and health outcomes in longitudinal studies
dc.contributor.authorElliott, Michael R.en_US
dc.contributor.authorSammel, Mary D.en_US
dc.contributor.authorFaul, Jessicaen_US
dc.date.accessioned2012-10-02T17:20:19Z
dc.date.available2013-11-04T19:53:16Zen_US
dc.date.issued2012-10-15en_US
dc.identifier.citationElliott, Michael R.; Sammel, Mary D.; Faul, Jessica (2012). "Associations between variability of risk factors and health outcomes in longitudinal studies." Statistics in Medicine 31(23): 2745-2756. <http://hdl.handle.net/2027.42/93730>en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/93730
dc.publisherCambridge University Pressen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherDifferential Measurement Erroren_US
dc.subject.otherMarkov Chain Monte Carloen_US
dc.subject.otherTotal Recallen_US
dc.subject.otherDementiaen_US
dc.subject.otherHealth and Retirement Surveyen_US
dc.titleAssociations between variability of risk factors and health outcomes in longitudinal studiesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.identifier.pmid22815213en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/93730/1/sim5370.pdf
dc.identifier.doi10.1002/sim.5370en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.identifier.citedreferenceJuster FT, Suzman R. An overview of the Health and Retirement Study. Journal of Human Resources 1995; 30: S7‐S56.en_US
dc.identifier.citedreferenceGelfand AE, Smith AMF. Sampling‐based approaches to calculating marginal densities. Journal of the American Statistical Association 1990; 85: 389 – 409.en_US
dc.identifier.citedreferenceGelman A, Carlin J, Stern H, Rubin D. Bayesian Data Analysis, 2nd Edition. Chapman & Hall/CRC: Boca Raton, FL, 2004.en_US
dc.identifier.citedreferenceGilks WR, Wild P. Adaptive rejection sampling for Gibbs sampling. Applied Statistics 1992; 41: 337 – 348.en_US
dc.identifier.citedreferenceDurrleman S, Simon R. Flexible regression models with cubic splines. Statistics in Medicine 1989; 8: 551 – 561.en_US
dc.identifier.citedreferenceHultsch DF, Hertzog C, Small BJ, McDonald‐Miszczak L, Dixon RA. Short‐term longitudinal change in cognitive performance in later life. Psychology and Aging 1992; 7: 571 – 584.en_US
dc.identifier.citedreferenceNesselroade JR, Intraindividual variability and short‐term change. Commentary. Gerontology 2004; 50: 44 – 47. DOI: 10.1159/000074389.en_US
dc.identifier.citedreferenceMartin M, Hofer SM. Intraindividual variability, change, and aging: conceptual and analytical issues. Gerontology 2004; 50: 7 – 11. DOI: 10.1159/000074382.en_US
dc.identifier.citedreferenceSchaie KW. The impact of longitudinal studies on understanding development from young adulthood to old age. International Journal of Behavioral Development 2000; 24: 257 – 266. DOI: 10.1159/000074382.en_US
dc.identifier.citedreferenceDarby D, Maruff P, Collie A, McStephen M. Mild cognitive impairment can be detected by multiple assessments in a single day. Neurology 2002; 59: 1042 – 1046.en_US
dc.identifier.citedreferenceKliegel M, Sliwinski M. MMSE cross‐domain variability predicts cognitive decline in centenarians. Gerontology 2004; 50: 39 – 43. DOI: 10.1159/000074388.en_US
dc.identifier.citedreferenceCollie A, Maruff P, Currie J. Behavioral characterization of mild cognitive impairment. Journal of Clinical and Experimental Neuropsychology 2002; 24: 720 – 733. DOI: 10.1076/jcen.24.6.720.8397.en_US
dc.identifier.citedreferenceTaylor Jr. D H, Ostbye T, Langa KM, Weir D, Plassman BL. The accuracy of Medicare claims as an epidemiological tool: the case of dementia revisited. Journal of Alzheimer's Disease 2009; 17: 807 – 15. DOI: 10.3233/JAD‐2009‐1099.en_US
dc.identifier.citedreferenceLittle RJA, Rubin DB. Statistical Analysis With Missing Data, 2nd edition. Wiley: New York, NY, 2002.en_US
dc.identifier.citedreferenceProust‐Lima C, Letenneur L, Jacqmin‐Gadda H. A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome. Statistics in Medicine 2007; 26: 2229 – 2245.en_US
dc.identifier.citedreferenceLin H, Turnbull BW, Mc Culloch CE, Slate EH. Latent class models for joint analysis of longitudinal biomarker and event process data: application to longitudinal prostate‐specific antigen readings and prostate cancer. Journal of the American Statistical Association 2002; 97: 5365.en_US
dc.identifier.citedreferenceGelman A, Meng X‐L, Stern H. Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica 1996; 6: 733 – 807.en_US
dc.identifier.citedreferenceBarnard J, McCulloch R, Meng X‐L. Modeling covariance matrices in terms of standard deviations and correlations, with applications to shrinkage. Statisticia Sinica 2000; 10: 1281 – 1311.en_US
dc.identifier.citedreferenceCarrol RJ. Variances are not always nuisance parameters. Biometrics 2003; 59: 211 – 220.en_US
dc.identifier.citedreferenceHarlow SD, Lin X. Ho MJ. Analysis of menstrual diary data across the reproductive life span: applicability of the bipartite model approach and the importance of within‐woman variance. Journal of Clinical Epidemiology 2000; 53: 722 – 733. DOI: 10.1016/S0895‐4356(99)00202‐4.en_US
dc.identifier.citedreferenceSammel MD, Wang Y, Ratcliffe SJ, Freeman E, Propert KJ. Models for within‐subject heterogeneity as predictors for disease. Proceedings of The American Statistical Association, Biometrics Section, Atlanta GA, 2001.en_US
dc.identifier.citedreferenceElliott MR. Identifying latent clusters of variability in longitudinal data. Biostatistics 2007; 8: 756 – 771. DOI: 10.1093/biostatistics/kxm003.en_US
dc.identifier.citedreferenceKikuya M, Ohkubo T, Metoki H, Asayama K, Hara A, Obara T, Inoue R, Hoshi H, Hashimoto J, Totsune K, Satoh H, Imai Y. Day‐by‐day variability of blood pressure and heart rate at home as a novel predictor of prognosis: the Ohasama study. Hypertension 2008; 52: 1045 – 1050. DOI: 10.1161/HYPERTENSIONAHA.107.104620.en_US
dc.identifier.citedreferenceFouque JP, Papanicolaou G, Sircar KR. Derivatives in Financial Markets with Stochastic Volatility. Cambridge University Press: Cambridge UK, 2000.en_US
dc.identifier.citedreferenceMuthén B, Brown CH, Masyn BJ, Khoo S‐T, Wang C‐P, Kellman SG, Carlin J, Liao J. General growth mixture modeling for randomized preventive interventions. Biostatistics 2002; 3: 459 – 475. DOI: 10.1093/biostatistics/3.4.459.en_US
dc.identifier.citedreferenceYe W, Lin X, Taylor JMG. Semiparametric modeling of longitudinal measurements and time‐to‐event data – a two‐stage regression calibration approach. Biometrics 2008; 64: 1238 – 1246. DOI: 10.1111/j.1541‐0420.2007.00983.x.en_US
dc.identifier.citedreferenceAnstey KJ, Smith GA. Interrelationships among biological markers of aging, health, activity, acculturation, and cognitive performance in late adulthood. Psychology and Aging 1999; 14: 605 – 618.en_US
dc.identifier.citedreferenceChristensen H, Mackinnon AJ, Korten AE, Jorm AF, Henderson AS, Jacomb P, Rodgers B. An analysis of diversity in the cognitive performance of elderly community dwellers: individual differences in change scores as a function of age. Psychology and Aging 1999; 14: 365 – 379.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
sim5370.pdf
Size:
322.5KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.