Literature DB >> 8337550

Measurement error in dietary assessment: an investigation using covariance structure models. Part II.

M Plummer1, D Clayton.   

Abstract

In Part I we presented a covariance structure model for analysing measurement error in the assessment of nitrogen intake. In this paper we include data on urine nitrogen excretion which allows a critical assessment of the model proposed. Inclusion of urine nitrogen data produces more pessimistic estimates of the quality of dietary intake measurements and shows that previous assumptions about independence of measurement error were wrong. This underscores the need for well founded assumptions in the analysis of measurement error.

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Year:  1993        PMID: 8337550     DOI: 10.1002/sim.4780121005

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  7 in total

1.  Addressing Current Criticism Regarding the Value of Self-Report Dietary Data.

Authors:  Amy F Subar; Laurence S Freedman; Janet A Tooze; Sharon I Kirkpatrick; Carol Boushey; Marian L Neuhouser; Frances E Thompson; Nancy Potischman; Patricia M Guenther; Valerie Tarasuk; Jill Reedy; Susan M Krebs-Smith
Journal:  J Nutr       Date:  2015-10-14       Impact factor: 4.798

Review 2.  Considering the value of dietary assessment data in informing nutrition-related health policy.

Authors:  James R Hébert; Thomas G Hurley; Susan E Steck; Donald R Miller; Fred K Tabung; Karen E Peterson; Lawrence H Kushi; Edward A Frongillo
Journal:  Adv Nutr       Date:  2014-07-14       Impact factor: 8.701

3.  Methods to Assess Measurement Error in Questionnaires of Sedentary Behavior.

Authors:  Joshua N Sampson; Charles E Matthews; Laurence Freedman; Raymond J Carroll; Victor Kipnis
Journal:  J Appl Stat       Date:  2016-03-17       Impact factor: 1.404

4.  Measurement error correction for nutritional exposures with correlated measurement error: use of the method of triads in a longitudinal setting.

Authors:  Bernard Rosner; Karin B Michels; Ya-Hua Chen; Nicholas E Day
Journal:  Stat Med       Date:  2008-08-15       Impact factor: 2.373

5.  Fat, protein, and meat consumption and renal cell cancer risk: a pooled analysis of 13 prospective studies.

Authors:  Jung Eun Lee; Donna Spiegelman; David J Hunter; Demetrius Albanes; Leslie Bernstein; Piet A van den Brandt; Julie E Buring; Eunyoung Cho; Dallas R English; Jo L Freudenheim; Graham G Giles; Saxon Graham; Pamela L Horn-Ross; Niclas Håkansson; Michael F Leitzmann; Satu Männistö; Marjorie L McCullough; Anthony B Miller; Alexander S Parker; Thomas E Rohan; Arthur Schatzkin; Leo J Schouten; Carol Sweeney; Walter C Willett; Alicja Wolk; Shumin M Zhang; Stephanie A Smith-Warner
Journal:  J Natl Cancer Inst       Date:  2008-11-25       Impact factor: 13.506

6.  Lifetime body size and reproductive factors: comparisons of data recorded prospectively with self reports in middle age.

Authors:  Benjamin J Cairns; Bette Liu; Suzanne Clennell; Rachel Cooper; Gillian K Reeves; Valerie Beral; Diana Kuh
Journal:  BMC Med Res Methodol       Date:  2011-01-17       Impact factor: 4.615

7.  Validation of the Oxford WebQ Online 24-Hour Dietary Questionnaire Using Biomarkers.

Authors:  Darren C Greenwood; Laura J Hardie; Gary S Frost; Nisreen A Alwan; Kathryn E Bradbury; Michelle Carter; Paul Elliott; Charlotte E L Evans; Heather E Ford; Neil Hancock; Timothy J Key; Bette Liu; Michelle A Morris; Umme Z Mulla; Katerina Petropoulou; Gregory D M Potter; Elio Riboli; Heather Young; Petra A Wark; Janet E Cade
Journal:  Am J Epidemiol       Date:  2019-10-01       Impact factor: 4.897

  7 in total

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