| pubmed-article:16332425 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:16332425 | lifeskim:mentions | umls-concept:C0026339 | lld:lifeskim |
| pubmed-article:16332425 | lifeskim:mentions | umls-concept:C0026336 | lld:lifeskim |
| pubmed-article:16332425 | lifeskim:mentions | umls-concept:C0684321 | lld:lifeskim |
| pubmed-article:16332425 | lifeskim:mentions | umls-concept:C0220825 | lld:lifeskim |
| pubmed-article:16332425 | lifeskim:mentions | umls-concept:C1519941 | lld:lifeskim |
| pubmed-article:16332425 | lifeskim:mentions | umls-concept:C1510438 | lld:lifeskim |
| pubmed-article:16332425 | lifeskim:mentions | umls-concept:C0449445 | lld:lifeskim |
| pubmed-article:16332425 | pubmed:issue | 1 | lld:pubmed |
| pubmed-article:16332425 | pubmed:dateCreated | 2006-3-20 | lld:pubmed |
| pubmed-article:16332425 | 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. | lld:pubmed |
| pubmed-article:16332425 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16332425 | pubmed:language | eng | lld:pubmed |
| pubmed-article:16332425 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16332425 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:16332425 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16332425 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16332425 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:16332425 | pubmed:month | Apr | lld:pubmed |
| pubmed-article:16332425 | pubmed:issn | 0731-7085 | lld:pubmed |
| pubmed-article:16332425 | pubmed:author | pubmed-author:AshtonMM | lld:pubmed |
| pubmed-article:16332425 | pubmed:author | pubmed-author:WhiteN JNJ | lld:pubmed |
| pubmed-article:16332425 | pubmed:author | pubmed-author:BergqvistYY | lld:pubmed |
| pubmed-article:16332425 | pubmed:author | pubmed-author:DayN P JNP | lld:pubmed |
| pubmed-article:16332425 | pubmed:author | pubmed-author:AnnerbergAA | lld:pubmed |
| pubmed-article:16332425 | pubmed:author | pubmed-author:SingtorojTT | lld:pubmed |
| pubmed-article:16332425 | pubmed:author | pubmed-author:TarningJJ | lld:pubmed |
| pubmed-article:16332425 | pubmed:author | pubmed-author:LindegardhNN | lld:pubmed |
| pubmed-article:16332425 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:16332425 | pubmed:day | 11 | lld:pubmed |
| pubmed-article:16332425 | pubmed:volume | 41 | lld:pubmed |
| pubmed-article:16332425 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:16332425 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:16332425 | pubmed:pagination | 219-27 | lld:pubmed |
| pubmed-article:16332425 | pubmed:dateRevised | 2009-11-19 | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
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| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:meshHeading | pubmed-meshheading:16332425... | lld:pubmed |
| pubmed-article:16332425 | pubmed:year | 2006 | lld:pubmed |
| pubmed-article:16332425 | pubmed:articleTitle | A new approach to evaluate regression models during validation of bioanalytical assays. | lld:pubmed |
| pubmed-article:16332425 | pubmed:affiliation | Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand. | lld:pubmed |
| pubmed-article:16332425 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:16332425 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:16332425 | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:16332425 | lld:pubmed |
