The Effect of Variance Function Estimation on Nonlinear Calibration Inference in Immunoassay Data
Often with data from immunoassays, the concentration-response relationship is nonlinear and intra-assay response variance is heterogeneous. Estimation of the standard curve is usually based on a nonlinear heteroscedastic regression model for concentration-response, where variance is modeled as a fun...
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| Veröffentlicht in: | Biometrics 1996-03, Vol.52 (1), p.158-175 |
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| container_title | Biometrics |
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| creator | Belanger, Bruce A. Davidian, Marie Giltinan, David M. |
| description | Often with data from immunoassays, the concentration-response relationship is nonlinear and intra-assay response variance is heterogeneous. Estimation of the standard curve is usually based on a nonlinear heteroscedastic regression model for concentration-response, where variance is modeled as a function of mean response and additional variance parameters. This paper discusses calibration inference for immunoassay data which exhibit this nonlinear heteroscedastic mean-variance relationship. An assessment of the effect of variance function estimation in three types of approximate large-sample confidence intervals for unknown concentrations is given by theoretical and empirical investigation and application to two examples. A major finding is that the accuracy of such calibration intervals depends critically on the nature of response variance and the quality with which variance parameters are estimated. |
| doi_str_mv | 10.2307/2533153 |
| format | Article |
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A major finding is that the accuracy of such calibration intervals depends critically on the nature of response variance and the quality with which variance parameters are estimated.</description><identifier>ISSN: 0006-341X</identifier><identifier>EISSN: 1541-0420</identifier><identifier>DOI: 10.2307/2533153</identifier><identifier>PMID: 8934590</identifier><language>eng</language><publisher>United States: International Biometric Society</publisher><subject>Algorithms ; Analysis of Variance ; Animals ; Biometrics ; Biometry - methods ; Calibration ; Computer Simulation ; Confidence interval ; Data Interpretation, Statistical ; Enzyme-Linked Immunosorbent Assay - standards ; Enzyme-Linked Immunosorbent Assay - statistics & numerical data ; Estimation methods ; Humans ; Immunoassay - standards ; Immunoassay - statistics & numerical data ; Inference ; Interval estimators ; Mathematical independent variables ; Monte Carlo Method ; Musical intervals ; Nonlinear Dynamics ; Pharmaceutical Preparations - analysis ; Pharmaceutical Preparations - standards ; Radioimmunoassay - standards ; Radioimmunoassay - statistics & numerical data ; Recombinant Proteins - analysis ; Reference Standards ; Regression analysis ; Relaxin - analysis ; Statistical variance ; Swine</subject><ispartof>Biometrics, 1996-03, Vol.52 (1), p.158-175</ispartof><rights>Copyright 1996 The International Biometric Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c305t-c8a30808dddaf4034c3c3d753c23562efc1fa9dbf03abc8e2dbbadc41142e2013</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/2533153$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/2533153$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,832,27924,27925,58017,58021,58250,58254</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/8934590$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Belanger, Bruce A.</creatorcontrib><creatorcontrib>Davidian, Marie</creatorcontrib><creatorcontrib>Giltinan, David M.</creatorcontrib><title>The Effect of Variance Function Estimation on Nonlinear Calibration Inference in Immunoassay Data</title><title>Biometrics</title><addtitle>Biometrics</addtitle><description>Often with data from immunoassays, the concentration-response relationship is nonlinear and intra-assay response variance is heterogeneous. Estimation of the standard curve is usually based on a nonlinear heteroscedastic regression model for concentration-response, where variance is modeled as a function of mean response and additional variance parameters. This paper discusses calibration inference for immunoassay data which exhibit this nonlinear heteroscedastic mean-variance relationship. An assessment of the effect of variance function estimation in three types of approximate large-sample confidence intervals for unknown concentrations is given by theoretical and empirical investigation and application to two examples. A major finding is that the accuracy of such calibration intervals depends critically on the nature of response variance and the quality with which variance parameters are estimated.</description><subject>Algorithms</subject><subject>Analysis of Variance</subject><subject>Animals</subject><subject>Biometrics</subject><subject>Biometry - methods</subject><subject>Calibration</subject><subject>Computer Simulation</subject><subject>Confidence interval</subject><subject>Data Interpretation, Statistical</subject><subject>Enzyme-Linked Immunosorbent Assay - standards</subject><subject>Enzyme-Linked Immunosorbent Assay - statistics & numerical data</subject><subject>Estimation methods</subject><subject>Humans</subject><subject>Immunoassay - standards</subject><subject>Immunoassay - statistics & numerical data</subject><subject>Inference</subject><subject>Interval estimators</subject><subject>Mathematical independent variables</subject><subject>Monte Carlo Method</subject><subject>Musical intervals</subject><subject>Nonlinear Dynamics</subject><subject>Pharmaceutical Preparations - analysis</subject><subject>Pharmaceutical Preparations - standards</subject><subject>Radioimmunoassay - standards</subject><subject>Radioimmunoassay - statistics & numerical data</subject><subject>Recombinant Proteins - analysis</subject><subject>Reference Standards</subject><subject>Regression analysis</subject><subject>Relaxin - analysis</subject><subject>Statistical variance</subject><subject>Swine</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1996</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1UE1Lw0AUXESptYq_QMhB9BR9-9UmR6mtFopeqngLL_uBKcmu7iaH_ntTU7wJD94bZt7ADCGXFO4Yh9k9k5xTyY_ImEpBUxAMjskYAKYpF_TjlJzFuO1hLoGNyCjLuZA5jAluPk2ysNaoNvE2ecdQoVMmWXZOtZV3ySK2VYO_Zz8v3tWVMxiSOdZVGQZi5awJZv9W9aBpOucxRtwlj9jiOTmxWEdzcdgT8rZcbObP6fr1aTV_WKeKg2xTlSGHDDKtNVoBXCiuuJ5JrhiXU2asohZzXVrgWKrMMF2WqJWgVDDDgPIJuRl8v4L_7kxsi6aKytQ1OuO7WMwyKffxe-HtIFTBxxiMLb5CHzHsCgrFvsziUGavvDpYdmVj9J_u0F7PXw_8NrY-_GvzA2yfemo</recordid><startdate>19960301</startdate><enddate>19960301</enddate><creator>Belanger, Bruce A.</creator><creator>Davidian, Marie</creator><creator>Giltinan, David M.</creator><general>International Biometric Society</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>19960301</creationdate><title>The Effect of Variance Function Estimation on Nonlinear Calibration Inference in Immunoassay Data</title><author>Belanger, Bruce A. ; Davidian, Marie ; Giltinan, David M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c305t-c8a30808dddaf4034c3c3d753c23562efc1fa9dbf03abc8e2dbbadc41142e2013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Algorithms</topic><topic>Analysis of Variance</topic><topic>Animals</topic><topic>Biometrics</topic><topic>Biometry - methods</topic><topic>Calibration</topic><topic>Computer Simulation</topic><topic>Confidence interval</topic><topic>Data Interpretation, Statistical</topic><topic>Enzyme-Linked Immunosorbent Assay - standards</topic><topic>Enzyme-Linked Immunosorbent Assay - statistics & numerical data</topic><topic>Estimation methods</topic><topic>Humans</topic><topic>Immunoassay - standards</topic><topic>Immunoassay - statistics & numerical data</topic><topic>Inference</topic><topic>Interval estimators</topic><topic>Mathematical independent variables</topic><topic>Monte Carlo Method</topic><topic>Musical intervals</topic><topic>Nonlinear Dynamics</topic><topic>Pharmaceutical Preparations - analysis</topic><topic>Pharmaceutical Preparations - standards</topic><topic>Radioimmunoassay - standards</topic><topic>Radioimmunoassay - statistics & numerical data</topic><topic>Recombinant Proteins - analysis</topic><topic>Reference Standards</topic><topic>Regression analysis</topic><topic>Relaxin - analysis</topic><topic>Statistical variance</topic><topic>Swine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Belanger, Bruce A.</creatorcontrib><creatorcontrib>Davidian, Marie</creatorcontrib><creatorcontrib>Giltinan, David M.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Biometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Belanger, Bruce A.</au><au>Davidian, Marie</au><au>Giltinan, David M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Effect of Variance Function Estimation on Nonlinear Calibration Inference in Immunoassay Data</atitle><jtitle>Biometrics</jtitle><addtitle>Biometrics</addtitle><date>1996-03-01</date><risdate>1996</risdate><volume>52</volume><issue>1</issue><spage>158</spage><epage>175</epage><pages>158-175</pages><issn>0006-341X</issn><eissn>1541-0420</eissn><abstract>Often with data from immunoassays, the concentration-response relationship is nonlinear and intra-assay response variance is heterogeneous. Estimation of the standard curve is usually based on a nonlinear heteroscedastic regression model for concentration-response, where variance is modeled as a function of mean response and additional variance parameters. This paper discusses calibration inference for immunoassay data which exhibit this nonlinear heteroscedastic mean-variance relationship. An assessment of the effect of variance function estimation in three types of approximate large-sample confidence intervals for unknown concentrations is given by theoretical and empirical investigation and application to two examples. A major finding is that the accuracy of such calibration intervals depends critically on the nature of response variance and the quality with which variance parameters are estimated.</abstract><cop>United States</cop><pub>International Biometric Society</pub><pmid>8934590</pmid><doi>10.2307/2533153</doi><tpages>18</tpages></addata></record> |
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| ispartof | Biometrics, 1996-03, Vol.52 (1), p.158-175 |
| issn | 0006-341X 1541-0420 |
| language | eng |
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| source | MEDLINE; JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing |
| subjects | Algorithms Analysis of Variance Animals Biometrics Biometry - methods Calibration Computer Simulation Confidence interval Data Interpretation, Statistical Enzyme-Linked Immunosorbent Assay - standards Enzyme-Linked Immunosorbent Assay - statistics & numerical data Estimation methods Humans Immunoassay - standards Immunoassay - statistics & numerical data Inference Interval estimators Mathematical independent variables Monte Carlo Method Musical intervals Nonlinear Dynamics Pharmaceutical Preparations - analysis Pharmaceutical Preparations - standards Radioimmunoassay - standards Radioimmunoassay - statistics & numerical data Recombinant Proteins - analysis Reference Standards Regression analysis Relaxin - analysis Statistical variance Swine |
| title | The Effect of Variance Function Estimation on Nonlinear Calibration Inference in Immunoassay Data |
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