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A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD
We consider a new Bayesian approach for heterogeneity that can take into account categorical covariates, if available. We use the Genetic Analysis Workshop 14 simulated data to first compare the Bayesian approach with the heterogeneity LOD, when no covariate information is used. We find that the for...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2005
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866798/ https://www.ncbi.nlm.nih.gov/pubmed/16451597 http://dx.doi.org/10.1186/1471-2156-6-S1-S138 |
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author | Biswas, Swati Lin, Shili Berry, Donald A |
author_facet | Biswas, Swati Lin, Shili Berry, Donald A |
author_sort | Biswas, Swati |
collection | PubMed |
description | We consider a new Bayesian approach for heterogeneity that can take into account categorical covariates, if available. We use the Genetic Analysis Workshop 14 simulated data to first compare the Bayesian approach with the heterogeneity LOD, when no covariate information is used. We find that the former is more powerful, while the two approaches have comparable false-positive rates. We then include informative covariates in the Bayesian approach and find that it tends to give more precise interval estimates of the disease gene location than when covariates are not included. We had knowledge of the simulation models at the time we performed the analyses. |
format | Text |
id | pubmed-1866798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2005 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-18667982007-05-11 A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD Biswas, Swati Lin, Shili Berry, Donald A BMC Genet Proceedings We consider a new Bayesian approach for heterogeneity that can take into account categorical covariates, if available. We use the Genetic Analysis Workshop 14 simulated data to first compare the Bayesian approach with the heterogeneity LOD, when no covariate information is used. We find that the former is more powerful, while the two approaches have comparable false-positive rates. We then include informative covariates in the Bayesian approach and find that it tends to give more precise interval estimates of the disease gene location than when covariates are not included. We had knowledge of the simulation models at the time we performed the analyses. BioMed Central 2005-12-30 /pmc/articles/PMC1866798/ /pubmed/16451597 http://dx.doi.org/10.1186/1471-2156-6-S1-S138 Text en Copyright © 2005 Biswas et al; licensee BioMed Central Ltd http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Biswas, Swati Lin, Shili Berry, Donald A A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD |
title | A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD |
title_full | A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD |
title_fullStr | A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD |
title_full_unstemmed | A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD |
title_short | A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD |
title_sort | new bayesian approach incorporating covariate information for heterogeneity and its comparison with hlod |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866798/ https://www.ncbi.nlm.nih.gov/pubmed/16451597 http://dx.doi.org/10.1186/1471-2156-6-S1-S138 |
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