Show simple item record

dc.contributor.authorStanek, Edward J.
dc.contributor.authorWell, Arnold D.
dc.contributor.authorOckene, Ira S.
dc.date2022-08-11T08:08:02.000
dc.date.accessioned2022-08-23T15:40:23Z
dc.date.available2022-08-23T15:40:23Z
dc.date.issued1999-10-19
dc.date.submitted2008-04-11
dc.identifier.citation<p>Stat Med. 1999 Nov 15;18(21):2943-59.</p>
dc.identifier.issn0277-6715 (Print)
dc.identifier.doi10.1002/(SICI)1097-0258(19991115)18:21<2943::AID-SIM241>3.0.CO;2-0
dc.identifier.pmid10523752
dc.identifier.urihttp://hdl.handle.net/20.500.14038/26390
dc.description.abstractMeasures of biologic and behavioural variables on a patient often estimate longer term latent values, with the two connected by a simple response error model. For example, a subject's measured total cholesterol is an estimate (equal to the best linear unbiased estimate (BLUE)) of a subject's latent total cholesterol. With known (or estimated) variances, an alternative estimate is the best linear unbiased predictor (BLUP). We illustrate and discuss when the BLUE or BLUP will be a better estimate of a subject's latent value given a single measure on a subject, concluding that the BLUP estimator should be routinely used for total cholesterol and per cent kcal from fat, with a modified BLUP estimator used for large observed values of leisure time activity. Data from a large longitudinal study of seasonal variation in serum cholesterol forms the backdrop for the illustrations. Simulations which mimic the empirical and response error distributions are used to guide choice of an estimator. We use the simulations to describe criteria for estimator choice, to identify parameter ranges where BLUE or BLUP estimates are superior, and discuss key ideas that underlie the results.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=10523752&dopt=Abstract ">Link to article in PubMed</a></p>
dc.relation.urlhttps://doi.org/10.1002/(SICI)1097-0258(19991115)18:21<2943::AID-SIM241>3.0.CO;2-0
dc.subjectAdult
dc.subjectAged
dc.subjectCholesterol
dc.subject*Computer Simulation
dc.subjectDietary Fats
dc.subjectExercise
dc.subjectFemale
dc.subjectHumans
dc.subjectMale
dc.subjectMetabolism
dc.subjectMiddle Aged
dc.subject*Models, Cardiovascular
dc.subject*Models, Statistical
dc.subject*Predictive Value of Tests
dc.subjectCardiology
dc.subjectCardiovascular Diseases
dc.subjectDiagnosis
dc.subjectLipids
dc.subjectPolycyclic Compounds
dc.titleWhy not routinely use best linear unbiased predictors (BLUPs) as estimates of cholesterol, per cent fat from kcal and physical activity
dc.typeArticle
dc.source.journaltitleStatistics in medicine
dc.source.volume18
dc.source.issue21
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/cardio_pp/73
dc.identifier.contextkey488369
html.description.abstract<p>Measures of biologic and behavioural variables on a patient often estimate longer term latent values, with the two connected by a simple response error model. For example, a subject's measured total cholesterol is an estimate (equal to the best linear unbiased estimate (BLUE)) of a subject's latent total cholesterol. With known (or estimated) variances, an alternative estimate is the best linear unbiased predictor (BLUP). We illustrate and discuss when the BLUE or BLUP will be a better estimate of a subject's latent value given a single measure on a subject, concluding that the BLUP estimator should be routinely used for total cholesterol and per cent kcal from fat, with a modified BLUP estimator used for large observed values of leisure time activity. Data from a large longitudinal study of seasonal variation in serum cholesterol forms the backdrop for the illustrations. Simulations which mimic the empirical and response error distributions are used to guide choice of an estimator. We use the simulations to describe criteria for estimator choice, to identify parameter ranges where BLUE or BLUP estimates are superior, and discuss key ideas that underlie the results.</p>
dc.identifier.submissionpathcardio_pp/73
dc.contributor.departmentDepartment of Medicine, Division of Cardiovascular Medicine
dc.source.pages2943-59


This item appears in the following Collection(s)

Show simple item record