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Assessment of biomarkers for risk prediction with nested case–control studies
Background Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools. Purpo...
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Published in: | Clinical trials (London, England) England), 2013-10, Vol.10 (5), p.677-679 |
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creator | Zhou, Qian M Zheng, Yingye Cai, Tianxi |
description | Background
Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools.
Purpose
In large prospective cohort studies, the standard full-cohort design, requiring marker measurement on the entire cohort, may be infeasible due to cost and low rate of the clinical condition of interest. To overcome such difficulties, nested case–control (NCC) studies provide cost-effective alternatives but bring about challenges in statistical analyses due to complex data sets generated.
Methods
To evaluate prognostic accuracy of a risk model, Cai and Zheng proposed a class of nonparametric inverse probability weighting (IPW) estimators for accuracy measures in the time-dependent receiver operating characteristic curve analysis. To accommodate a three-phase NCC design in Nurses’ Health Study, we extend the double IPW estimators of Cai and Zheng to develop risk prediction models under time-dependent generalized linear models and evaluate the incremental values of new biomarkers and genetic markers.
Results
Our results suggest that aggregating the information from both the genetic markers and biomarkers substantially improves the accuracy for predicting 5-year and 10-year risks of rheumatoid arthritis.
Conclusions
Our method provided robust procedures to evaluate the incremental value of new biomarkers allowing for complex sampling designs. |
doi_str_mv | 10.1177/1740774513498321 |
format | article |
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Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools.
Purpose
In large prospective cohort studies, the standard full-cohort design, requiring marker measurement on the entire cohort, may be infeasible due to cost and low rate of the clinical condition of interest. To overcome such difficulties, nested case–control (NCC) studies provide cost-effective alternatives but bring about challenges in statistical analyses due to complex data sets generated.
Methods
To evaluate prognostic accuracy of a risk model, Cai and Zheng proposed a class of nonparametric inverse probability weighting (IPW) estimators for accuracy measures in the time-dependent receiver operating characteristic curve analysis. To accommodate a three-phase NCC design in Nurses’ Health Study, we extend the double IPW estimators of Cai and Zheng to develop risk prediction models under time-dependent generalized linear models and evaluate the incremental values of new biomarkers and genetic markers.
Results
Our results suggest that aggregating the information from both the genetic markers and biomarkers substantially improves the accuracy for predicting 5-year and 10-year risks of rheumatoid arthritis.
Conclusions
Our method provided robust procedures to evaluate the incremental value of new biomarkers allowing for complex sampling designs.</description><identifier>ISSN: 1740-7745</identifier><identifier>EISSN: 1740-7753</identifier><identifier>DOI: 10.1177/1740774513498321</identifier><identifier>PMID: 24013405</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Arthritis, Rheumatoid - epidemiology ; Biomarkers ; Case-Control Studies ; Clinical Trials as Topic - methods ; Genetic markers ; Humans ; Probability ; Rheumatoid arthritis ; Risk Assessment ; ROC Curve</subject><ispartof>Clinical trials (London, England), 2013-10, Vol.10 (5), p.677-679</ispartof><rights>The Author(s), 2013</rights><rights>SAGE Publications © Oct 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c462t-a0fe6149970f4fa7a5e68dca82143a5607871d736bfcc41d35631e8a2fef32063</citedby><cites>FETCH-LOGICAL-c462t-a0fe6149970f4fa7a5e68dca82143a5607871d736bfcc41d35631e8a2fef32063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27923,27924,79135</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24013405$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhou, Qian M</creatorcontrib><creatorcontrib>Zheng, Yingye</creatorcontrib><creatorcontrib>Cai, Tianxi</creatorcontrib><title>Assessment of biomarkers for risk prediction with nested case–control studies</title><title>Clinical trials (London, England)</title><addtitle>Clin Trials</addtitle><description>Background
Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools.
Purpose
In large prospective cohort studies, the standard full-cohort design, requiring marker measurement on the entire cohort, may be infeasible due to cost and low rate of the clinical condition of interest. To overcome such difficulties, nested case–control (NCC) studies provide cost-effective alternatives but bring about challenges in statistical analyses due to complex data sets generated.
Methods
To evaluate prognostic accuracy of a risk model, Cai and Zheng proposed a class of nonparametric inverse probability weighting (IPW) estimators for accuracy measures in the time-dependent receiver operating characteristic curve analysis. To accommodate a three-phase NCC design in Nurses’ Health Study, we extend the double IPW estimators of Cai and Zheng to develop risk prediction models under time-dependent generalized linear models and evaluate the incremental values of new biomarkers and genetic markers.
Results
Our results suggest that aggregating the information from both the genetic markers and biomarkers substantially improves the accuracy for predicting 5-year and 10-year risks of rheumatoid arthritis.
Conclusions
Our method provided robust procedures to evaluate the incremental value of new biomarkers allowing for complex sampling designs.</description><subject>Arthritis, Rheumatoid - epidemiology</subject><subject>Biomarkers</subject><subject>Case-Control Studies</subject><subject>Clinical Trials as Topic - methods</subject><subject>Genetic markers</subject><subject>Humans</subject><subject>Probability</subject><subject>Rheumatoid arthritis</subject><subject>Risk Assessment</subject><subject>ROC Curve</subject><issn>1740-7745</issn><issn>1740-7753</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp1kbtKBDEUhoMo3nsrCdjYjCaTZDLbCCLeQLDROmQzJxqdnaw5M4qd7-Ab-iRmWV1UsEpIvv8_l5-QHc4OONf6kGvJtJaKCzmqRcmXyPrsqdBaieXFXao1soH4wFhZq1qskrVSsixhap1cHyMC4gS6nkZPxyFObHqEhNTHRFPARzpN0ATXh9jRl9Df0w6wh4Y6i_Dx9u5i16fYUuyHJgBukRVvW4Ttr3OT3J6d3pxcFFfX55cnx1eFk1XZF5Z5qLgcjTTz0lttFVR142xdcimsqpiuNW-0qMbeOckboSrBobalBy9KVolNcjT3nQ7jCTQu959sa6Yp5P5fTbTB_P7pwr25i89G1HkNQmSD_S-DFJ-GPJKZBHTQtraDOKDhUgpZqrzUjO79QR_ikLo83oySTFZayEyxOeVSREzgF81wZmZpmb9pZcnuzyEWgu94MlDMAbR38KPqf4afltud9w</recordid><startdate>20131001</startdate><enddate>20131001</enddate><creator>Zhou, Qian M</creator><creator>Zheng, Yingye</creator><creator>Cai, Tianxi</creator><general>SAGE Publications</general><general>Sage Publications Ltd</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>3V.</scope><scope>7RV</scope><scope>7TK</scope><scope>7TS</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PADUT</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20131001</creationdate><title>Assessment of biomarkers for risk prediction with nested case–control studies</title><author>Zhou, Qian M ; Zheng, Yingye ; Cai, Tianxi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c462t-a0fe6149970f4fa7a5e68dca82143a5607871d736bfcc41d35631e8a2fef32063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Arthritis, Rheumatoid - epidemiology</topic><topic>Biomarkers</topic><topic>Case-Control Studies</topic><topic>Clinical Trials as Topic - methods</topic><topic>Genetic markers</topic><topic>Humans</topic><topic>Probability</topic><topic>Rheumatoid arthritis</topic><topic>Risk Assessment</topic><topic>ROC Curve</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Qian M</creatorcontrib><creatorcontrib>Zheng, Yingye</creatorcontrib><creatorcontrib>Cai, Tianxi</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Physical Education Index</collection><collection>Toxicology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>British Nursing Database</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Research Library China</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Clinical trials (London, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Qian M</au><au>Zheng, Yingye</au><au>Cai, Tianxi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of biomarkers for risk prediction with nested case–control studies</atitle><jtitle>Clinical trials (London, England)</jtitle><addtitle>Clin Trials</addtitle><date>2013-10-01</date><risdate>2013</risdate><volume>10</volume><issue>5</issue><spage>677</spage><epage>679</epage><pages>677-679</pages><issn>1740-7745</issn><eissn>1740-7753</eissn><abstract>Background
Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools.
Purpose
In large prospective cohort studies, the standard full-cohort design, requiring marker measurement on the entire cohort, may be infeasible due to cost and low rate of the clinical condition of interest. To overcome such difficulties, nested case–control (NCC) studies provide cost-effective alternatives but bring about challenges in statistical analyses due to complex data sets generated.
Methods
To evaluate prognostic accuracy of a risk model, Cai and Zheng proposed a class of nonparametric inverse probability weighting (IPW) estimators for accuracy measures in the time-dependent receiver operating characteristic curve analysis. To accommodate a three-phase NCC design in Nurses’ Health Study, we extend the double IPW estimators of Cai and Zheng to develop risk prediction models under time-dependent generalized linear models and evaluate the incremental values of new biomarkers and genetic markers.
Results
Our results suggest that aggregating the information from both the genetic markers and biomarkers substantially improves the accuracy for predicting 5-year and 10-year risks of rheumatoid arthritis.
Conclusions
Our method provided robust procedures to evaluate the incremental value of new biomarkers allowing for complex sampling designs.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><pmid>24013405</pmid><doi>10.1177/1740774513498321</doi><tpages>3</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Arthritis, Rheumatoid - epidemiology Biomarkers Case-Control Studies Clinical Trials as Topic - methods Genetic markers Humans Probability Rheumatoid arthritis Risk Assessment ROC Curve |
title | Assessment of biomarkers for risk prediction with nested case–control studies |
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