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A Bayesian approach to model the conditional correlation between several diagnostic tests and various replicated subjects measurements
Two key aims of diagnostic research are to accurately and precisely estimate disease prevalence and test sensitivity and specificity. Latent class models have been proposed that consider the correlation between subject measures determined by different tests in order to diagnose diseases for which go...
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| Published in: | Statistics in medicine 2017-09, Vol.36 (20), p.3154-3170 |
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| Main Authors: | , , , , |
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| container_end_page | 3170 |
| container_issue | 20 |
| container_start_page | 3154 |
| container_title | Statistics in medicine |
| container_volume | 36 |
| creator | Pereira da Silva, Hélio Doyle Ascaso, Carlos Gonçalves, Alessandra Queiroga Orlandi, Patricia Puccinelli Abellana, Rosa |
| description | Two key aims of diagnostic research are to accurately and precisely estimate disease prevalence and test sensitivity and specificity. Latent class models have been proposed that consider the correlation between subject measures determined by different tests in order to diagnose diseases for which gold standard tests are not available. In some clinical studies, several measures of the same subject are made with the same test under the same conditions (replicated measurements), and thus, replicated measurements for each subject are not independent. In the present study, we propose an extension of the Bayesian latent class Gaussian random effects model to fit the data with binary outcomes for tests with replicated subject measures. We describe an application using data collected on hookworm infection carried out in the municipality of Presidente Figueiredo, Amazonas State, Brazil. In addition, the performance of the proposed model was compared with that of current models (the subject random effects model and the conditional (in)dependent model) through a simulation study. As expected, the proposed model presented better accuracy and precision in the estimations of prevalence, sensitivity and specificity. Copyright © 2017 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/sim.7339 |
| format | article |
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Latent class models have been proposed that consider the correlation between subject measures determined by different tests in order to diagnose diseases for which gold standard tests are not available. In some clinical studies, several measures of the same subject are made with the same test under the same conditions (replicated measurements), and thus, replicated measurements for each subject are not independent. In the present study, we propose an extension of the Bayesian latent class Gaussian random effects model to fit the data with binary outcomes for tests with replicated subject measures. We describe an application using data collected on hookworm infection carried out in the municipality of Presidente Figueiredo, Amazonas State, Brazil. In addition, the performance of the proposed model was compared with that of current models (the subject random effects model and the conditional (in)dependent model) through a simulation study. 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Latent class models have been proposed that consider the correlation between subject measures determined by different tests in order to diagnose diseases for which gold standard tests are not available. In some clinical studies, several measures of the same subject are made with the same test under the same conditions (replicated measurements), and thus, replicated measurements for each subject are not independent. In the present study, we propose an extension of the Bayesian latent class Gaussian random effects model to fit the data with binary outcomes for tests with replicated subject measures. We describe an application using data collected on hookworm infection carried out in the municipality of Presidente Figueiredo, Amazonas State, Brazil. In addition, the performance of the proposed model was compared with that of current models (the subject random effects model and the conditional (in)dependent model) through a simulation study. As expected, the proposed model presented better accuracy and precision in the estimations of prevalence, sensitivity and specificity. Copyright © 2017 John Wiley & Sons, Ltd.</description><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bayesian approach</subject><subject>Bias</subject><subject>Biostatistics</subject><subject>Brazil - epidemiology</subject><subject>Computer Simulation</subject><subject>Cross-Sectional Studies - statistics & numerical data</subject><subject>diagnostic test</subject><subject>Diagnostic tests</subject><subject>Diagnostic Tests, Routine - statistics & numerical data</subject><subject>Epidemiology</subject><subject>Hookworm Infections - diagnosis</subject><subject>Hookworm Infections - epidemiology</subject><subject>Humans</subject><subject>latent class model</subject><subject>Likelihood Functions</subject><subject>Medical statistics</subject><subject>Models, Statistical</subject><subject>Prevalence</subject><subject>replicated measurement</subject><subject>sensitivity</subject><subject>specificity</subject><issn>0277-6715</issn><issn>1097-0258</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNpdkctKxDAUhoMoOl7AJ5CAGzfVXJomXergDRQX6rqk6VEztGlN0pF5AZ_blBlduDo5_B85_zk_QseUnFNC2EWw3bnkvNxCM0pKmREm1DaaESZlVkgq9tB-CAtCKBVM7qI9pkTOOZEz9H2Jr_QKgtUO62HwvTYfOPa46xtocfwAbHrX2Gh7p9v09h5aPXW4hvgF4HCAJfikNVa_uz5Ea3CEEAPWrsFL7W0_BuxhaK3RERocxnoBJukd6DB66MDFcIh23nQb4GhTD9DrzfXL_C57eLq9n18-ZIbToswMCKIAavpGa5FrDio3TIBJ-xYipzmrhYGSNdyQIpdKFDWRQkFuSi2VAsoP0Nn637Tp55hsVp0NBtpWO0g-K1qSNKhg5YSe_kMX_ejTFSaKJR8FVxN1sqHGuoOmGrzttF9VvxdOQLYGvmwLqz-dkmpKrkrJVVNy1fP941T5D44PjII</recordid><startdate>20170910</startdate><enddate>20170910</enddate><creator>Pereira da Silva, Hélio Doyle</creator><creator>Ascaso, Carlos</creator><creator>Gonçalves, Alessandra Queiroga</creator><creator>Orlandi, Patricia Puccinelli</creator><creator>Abellana, Rosa</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4487-4431</orcidid></search><sort><creationdate>20170910</creationdate><title>A Bayesian approach to model the conditional correlation between several diagnostic tests and various replicated subjects measurements</title><author>Pereira da Silva, Hélio Doyle ; 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Latent class models have been proposed that consider the correlation between subject measures determined by different tests in order to diagnose diseases for which gold standard tests are not available. In some clinical studies, several measures of the same subject are made with the same test under the same conditions (replicated measurements), and thus, replicated measurements for each subject are not independent. In the present study, we propose an extension of the Bayesian latent class Gaussian random effects model to fit the data with binary outcomes for tests with replicated subject measures. We describe an application using data collected on hookworm infection carried out in the municipality of Presidente Figueiredo, Amazonas State, Brazil. In addition, the performance of the proposed model was compared with that of current models (the subject random effects model and the conditional (in)dependent model) through a simulation study. 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| ispartof | Statistics in medicine, 2017-09, Vol.36 (20), p.3154-3170 |
| issn | 0277-6715 1097-0258 |
| language | eng |
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| source | Wiley-Blackwell Journals |
| subjects | Bayes Theorem Bayesian analysis Bayesian approach Bias Biostatistics Brazil - epidemiology Computer Simulation Cross-Sectional Studies - statistics & numerical data diagnostic test Diagnostic tests Diagnostic Tests, Routine - statistics & numerical data Epidemiology Hookworm Infections - diagnosis Hookworm Infections - epidemiology Humans latent class model Likelihood Functions Medical statistics Models, Statistical Prevalence replicated measurement sensitivity specificity |
| title | A Bayesian approach to model the conditional correlation between several diagnostic tests and various replicated subjects measurements |
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