Linked Life Data

17447938

Source:http://linkedlifedata.com/resource/pubmed/id/17447938

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2007-4-23
pubmed:abstractText
The relationship between nutrient consumption and chronic disease risk is the focus of a large number of epidemiological studies where food frequency questionnaires (FFQ) and food records are commonly used to assess dietary intake. However, these self-assessment tools are known to involve substantial random error for most nutrients, and probably important systematic error as well. Study subject selection in dietary intervention studies is sometimes conducted in two stages. At the first stage, FFQ-measured dietary intakes are observed and at the second stage another instrument, such as a 4-day food record, is administered only to participants who have fulfilled a prespecified criterion that is based on the baseline FFQ-measured dietary intake (e.g., only those reporting percent energy intake from fat above a prespecified quantity). Performing analysis without adjusting for this truncated sample design and for the measurement error in the nutrient consumption assessments will usually provide biased estimates for the population parameters. In this work we provide a general statistical analysis technique for such data with the classical additive measurement error that corrects for the two sources of bias. The proposed technique is based on multiple imputation for longitudinal data. Results of a simulation study along with a sensitivity analysis are presented, showing the performance of the proposed method under a simple linear regression model.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0006-341X
pubmed:author
pubmed:issnType
Print
pubmed:volume
63
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
137-42
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Linear measurement error models with restricted sampling.
pubmed:affiliation
Faculty of Industrial Engineering and Management, Technion-Israel Institute of Technology, Technion City, Haifa 3200, Israel. gorfinm@ie.technion.ac.il
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural