| Bayesian analysis of serial dilution assays. | |
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MedLine Citation:
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PMID: 15180666 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In a serial dilution assay, the concentration of a compound is estimated by combining measurements of several different dilutions of an unknown sample. The relation between concentration and measurement is nonlinear and heteroscedastic, and so it is not appropriate to weight these measurements equally. In the standard existing approach for analysis of these data, a large proportion of the measurements are discarded as being above or below detection limits. We present a Bayesian method for jointly estimating the calibration curve and the unknown concentrations using all the data. Compared to the existing method, our estimates have much lower standard errors and give estimates even when all the measurements are outside the "detection limits." We evaluate our method empirically using laboratory data on cockroach allergens measured in house dust samples. Our estimates are much more accurate than those obtained using the usual approach. In addition, we develop a method for determining the "effective weight" attached to each measurement, based on a local linearization of the estimated model. The effective weight can give insight into the information conveyed by each data point and suggests potential improvements in design of serial dilution experiments. |
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Authors:
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Andrew Gelman; Ginger L Chew; Michael Shnaidman |
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Publication Detail:
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Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S. |
Journal Detail:
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Title: Biometrics Volume: 60 ISSN: 0006-341X ISO Abbreviation: Biometrics Publication Date: 2004 Jun |
Date Detail:
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Created Date: 2004-06-07 Completed Date: 2005-01-14 Revised Date: 2007-11-14 |
Medline Journal Info:
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Nlm Unique ID: 0370625 Medline TA: Biometrics Country: United States |
Other Details:
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Languages: eng Pagination: 407-17 Citation Subset: IM |
Affiliation:
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Department of Statistics, Columbia University, New York 10027, USA. gelman@stat.columbia.edu |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Allergens
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analysis Animals Bayes Theorem* Biometry* Cockroaches / immunology Enzyme-Linked Immunosorbent Assay / statistics & numerical data Models, Statistical |
| Grant Support | |
ID/Acronym/Agency:
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1R01 ES10922-01A1/ES/NIEHS NIH HHS |
| Chemical | |
Reg. No./Substance:
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0/Allergens |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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