Estimation of the Distribution of Infection Times Using Longitudinal Serological Markers of HIV: Implications for the Estimation of HIV Incidence
In the last decade, interest has been focused on human immunodeficiency virus (HIV) antibody assays and testing strategies that could distinguish recent infections from established infection in a single serum sample. Incidence estimates are obtained by using the relationship between prevalence, inci...
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| Veröffentlicht in: | Biometrics 2011-06, Vol.67 (2), p.467-475 |
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| creator | Sommen, C. Commenges, D. Vu, S. Le Meyer, L. Alioum, A. |
| description | In the last decade, interest has been focused on human immunodeficiency virus (HIV) antibody assays and testing strategies that could distinguish recent infections from established infection in a single serum sample. Incidence estimates are obtained by using the relationship between prevalence, incidence, and duration of recent infection (window period). However, recent works demonstrated limitations of this approach due to the use of an estimated mean "window period." We propose an alternative approach that consists in estimating the distribution of infection times based on serological marker values at the moment when the infection is first discovered. We propose a model based on the repeated measurements of virological markers of seroconversion for the marker trajectory. The parameters of the model are estimated using data from a cohort of HIV-infected patients enrolled during primary infection. This model can be used for estimating the distribution of infection times for newly HIV diagnosed subjects reported in a HIV surveillance system. An approach is proposed for estimating HIV incidence from these results. |
| doi_str_mv | 10.1111/j.1541-0420.2010.01473.x |
| format | Article |
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Le ; Meyer, L. ; Alioum, A.</creator><creatorcontrib>Sommen, C. ; Commenges, D. ; Vu, S. Le ; Meyer, L. ; Alioum, A.</creatorcontrib><description>In the last decade, interest has been focused on human immunodeficiency virus (HIV) antibody assays and testing strategies that could distinguish recent infections from established infection in a single serum sample. Incidence estimates are obtained by using the relationship between prevalence, incidence, and duration of recent infection (window period). However, recent works demonstrated limitations of this approach due to the use of an estimated mean "window period." We propose an alternative approach that consists in estimating the distribution of infection times based on serological marker values at the moment when the infection is first discovered. We propose a model based on the repeated measurements of virological markers of seroconversion for the marker trajectory. The parameters of the model are estimated using data from a cohort of HIV-infected patients enrolled during primary infection. This model can be used for estimating the distribution of infection times for newly HIV diagnosed subjects reported in a HIV surveillance system. 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Le</creatorcontrib><creatorcontrib>Meyer, L.</creatorcontrib><creatorcontrib>Alioum, A.</creatorcontrib><title>Estimation of the Distribution of Infection Times Using Longitudinal Serological Markers of HIV: Implications for the Estimation of HIV Incidence</title><title>Biometrics</title><addtitle>Biometrics</addtitle><description>In the last decade, interest has been focused on human immunodeficiency virus (HIV) antibody assays and testing strategies that could distinguish recent infections from established infection in a single serum sample. Incidence estimates are obtained by using the relationship between prevalence, incidence, and duration of recent infection (window period). However, recent works demonstrated limitations of this approach due to the use of an estimated mean "window period." We propose an alternative approach that consists in estimating the distribution of infection times based on serological marker values at the moment when the infection is first discovered. We propose a model based on the repeated measurements of virological markers of seroconversion for the marker trajectory. The parameters of the model are estimated using data from a cohort of HIV-infected patients enrolled during primary infection. This model can be used for estimating the distribution of infection times for newly HIV diagnosed subjects reported in a HIV surveillance system. An approach is proposed for estimating HIV incidence from these results.</description><subject>AIDS</subject><subject>Antibodies</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>BIOMETRIC METHODOLOGY</subject><subject>Biometrics</subject><subject>Dynamic modeling</subject><subject>Estimating techniques</subject><subject>Estimation methods</subject><subject>HIV</subject><subject>HIV incidence</subject><subject>HIV infections</subject><subject>HIV Infections - diagnosis</subject><subject>HIV Infections - epidemiology</subject><subject>HIV Seropositivity</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Incidence</subject><subject>Infections</subject><subject>Longitudinal data</subject><subject>Longitudinal Studies</subject><subject>Medical research</subject><subject>Mixed model</subject><subject>Parametric models</subject><subject>Point estimators</subject><subject>Population Surveillance</subject><subject>Recent infections</subject><subject>Regression Analysis</subject><subject>Statistical Distributions</subject><subject>Surveillance</subject><subject>Surveillance system</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNks1u1DAUhS0EotPCI4AiNrDJ4L9JHBaVmLY0kaZ0QQvsLMe5GTwk8WAnYvoYvDHOpB0JFghv_HPO_a50jxGKCJ6TsN5u5mTBSYw5xXOKwysmPGXz3SM0OwiP0QxjnMSMk69H6Nj7TbhmC0yfoiOKU0YSns7Qrwvfm1b1xnaRraP-G0TnxvfOlMPDW9HVoPeXG9OCj2696dbRynZr0w-V6VQTfQJnG7s2OpyvlPsOzo-VefH5XVS02yYII8BHtXX7Hn92Db7QRZsKOg3P0JNaNR6e3-8n6PbDxc1ZHq-uL4uz96tYc5GxmAqaAa0WwEvNRApVRkoKtcCEKaHqusIlsDJRmAmuCE2DmGjCNSgKoqowO0GvJ-7W2R8D-F62xmtoGtWBHbwUKWNhqjQNzjf_dBJMsWAZwVmwvvrLurGDCyMaeVQwTOhoEpNJO-u9g1puXZiGuwskOeYrN3KMUY4xyjFfuc9X7kLpy3v-ULZQHQofAg2G08nw0zRw999guSyur8ZjALyYABvfW3cAcEI55YIHPZ708Etgd9BD6DJJWbqQXz5eyjzPl5yJpcTsN37nzSc</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Sommen, C.</creator><creator>Commenges, D.</creator><creator>Vu, S. Le</creator><creator>Meyer, L.</creator><creator>Alioum, A.</creator><general>Blackwell Publishing Inc</general><general>Wiley-Blackwell</general><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><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>JQ2</scope><scope>7U9</scope><scope>H94</scope><scope>7X8</scope></search><sort><creationdate>201106</creationdate><title>Estimation of the Distribution of Infection Times Using Longitudinal Serological Markers of HIV: Implications for the Estimation of HIV Incidence</title><author>Sommen, C. ; Commenges, D. ; Vu, S. 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| ispartof | Biometrics, 2011-06, Vol.67 (2), p.467-475 |
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| source | Oxford University Press Journals; MEDLINE; Wiley Online Library; JSTOR Mathematics and Statistics; JSTOR |
| subjects | AIDS Antibodies Bioinformatics Biomarkers Biomarkers - blood BIOMETRIC METHODOLOGY Biometrics Dynamic modeling Estimating techniques Estimation methods HIV HIV incidence HIV infections HIV Infections - diagnosis HIV Infections - epidemiology HIV Seropositivity Human immunodeficiency virus Humans Incidence Infections Longitudinal data Longitudinal Studies Medical research Mixed model Parametric models Point estimators Population Surveillance Recent infections Regression Analysis Statistical Distributions Surveillance Surveillance system |
| title | Estimation of the Distribution of Infection Times Using Longitudinal Serological Markers of HIV: Implications for the Estimation of HIV Incidence |
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