Linked Life Data

16332425

Source:http://linkedlifedata.com/resource/pubmed/id/16332425

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-3-20
pubmed:abstractText
The quality of bioanalytical data is highly dependent on using an appropriate regression model for calibration curves. Non-weighted linear regression has traditionally been used but is not necessarily the optimal model. Bioanalytical assays generally benefit from using either data transformation and/or weighting since variance normally increases with concentration. A data set with calibrators ranging from 9 to 10000 ng/mL was used to compare a new approach with the traditional approach for selecting an optimal regression model. The new approach used a combination of relative residuals at each calibration level together with precision and accuracy of independent quality control samples over 4 days to select and justify the best regression model. The results showed that log-log transformation without weighting was the simplest model to fit the calibration data and ensure good predictability for this data set.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0731-7085
pubmed:author
pubmed:issnType
Print
pubmed:day
11
pubmed:volume
41
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
219-27
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A new approach to evaluate regression models during validation of bioanalytical assays.
pubmed:affiliation
Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't