Document Detail


Bayesian analysis of serial dilution assays.
MedLine Citation:
PMID:  15180666     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Andrew Gelman; Ginger L Chew; Michael Shnaidman
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Biometrics     Volume:  60     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2004 Jun 
Date Detail:
Created Date:  2004-06-07     Completed Date:  2005-01-14     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  407-17     Citation Subset:  IM    
Affiliation:
Department of Statistics, Columbia University, New York 10027, USA. gelman@stat.columbia.edu
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MeSH Terms
Descriptor/Qualifier:
Allergens / analysis
Animals
Bayes Theorem*
Biometry*
Cockroaches / immunology
Enzyme-Linked Immunosorbent Assay / statistics & numerical data
Models, Statistical
Grant Support
ID/Acronym/Agency:
1R01 ES10922-01A1/ES/NIEHS NIH HHS
Chemical
Reg. No./Substance:
0/Allergens

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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