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Modeling Adverse Birth Outcomes via Confirmatory Factor Quantile Regression
We describe a Bayesian quantile regression model that uses a confirmatory factor structure for part of the design matrix. This model is appropriate when the covariates are indicators of scientifically determined latent factors, and it is these latent factors that analysts seek to include as predicto...
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| Published in: | Biometrics 2012-03, Vol.68 (1), p.92-100 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c4809-dd04a19c733fba502393e2c87211ad32ebf9eb89716caa6a84a76eef4e6e2c493 |
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| cites | cdi_FETCH-LOGICAL-c4809-dd04a19c733fba502393e2c87211ad32ebf9eb89716caa6a84a76eef4e6e2c493 |
| container_end_page | 100 |
| container_issue | 1 |
| container_start_page | 92 |
| container_title | Biometrics |
| container_volume | 68 |
| creator | Burgette, Lane F. Reiter, Jerome P. |
| description | We describe a Bayesian quantile regression model that uses a confirmatory factor structure for part of the design matrix. This model is appropriate when the covariates are indicators of scientifically determined latent factors, and it is these latent factors that analysts seek to include as predictors in the quantile regression. We apply the model to a study of birth weights in which the effects of latent variables representing psychosocial health and actual tobacco usage on the lower quantiles of the response distribution are of interest. The models can be fit using an R package called factorQR. |
| doi_str_mv | 10.1111/j.1541-0420.2011.01639.x |
| format | article |
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The models can be fit using an R package called factorQR.</description><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bayesian inference</subject><subject>Bayesian networks</subject><subject>BIOMETRIC METHODOLOGY</subject><subject>Biometrics</subject><subject>biometry</subject><subject>Birth Weight</subject><subject>Causality</subject><subject>Female</subject><subject>Fetal Growth Retardation - epidemiology</subject><subject>Gibbs sampling</subject><subject>Humans</subject><subject>Infant, Low Birth Weight</subject><subject>Infant, Newborn</subject><subject>Infant, Very Low Birth Weight</subject><subject>Low birth weight</subject><subject>Maternal Exposure - statistics & numerical data</subject><subject>Mathematical vectors</subject><subject>Modeling</subject><subject>Pregnancy</subject><subject>pregnancy complications</subject><subject>Prevalence</subject><subject>Proportional Hazards Models</subject><subject>Quantile regression</subject><subject>Random variables</subject><subject>Regression Analysis</subject><subject>tobacco</subject><subject>Tobacco Smoke Pollution - statistics & numerical data</subject><subject>Tobacco smoking</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNqNkEtv1DAURi0EokPhJwBRN6wSrh95eIPUjuhDdBjxqGBneZKbwSGJi520M_8eh5RZsMKba-sef74-hEQUEhrW2yahqaAxCAYJA0oToBmXye4RWRwaj8kCALKYC_r9iDzzvglHmQJ7So4YzQoJBSzIh5WtsDX9Njqt7tB5jM6MG35E63EobYc-ujM6Wtq-Nq7Tg3X76FyXoUafRt0PpsXoM24dem9s_5w8qXXr8cVDPSY35--_Li_j6_XF1fL0Oi5FATKuKhCayjLnvN7oMBCXHFlZ5IxSXXGGm1rippA5zUqtM10InWeItcAsYELyY_Jmzr119teIflCd8SW2re7Rjl5JET4nAxjIk3_Ixo6uD8MFSNCUg2QBKmaodNZ7h7W6dabTbq8oqEm3atRkVU1W1aRb_dGtduHqq4f8cdNhdbj4128A3s3AfVC1_-9gdXa1Xk3bEPByDmh8sH4IEFRwAYKGfjz3jR9wd-hr91NlOc9T9e3jhRIsTLNa5mriX898ra3SW2e8uvkSnk4BqMgLSPlv4-WvEQ</recordid><startdate>201203</startdate><enddate>201203</enddate><creator>Burgette, Lane F.</creator><creator>Reiter, Jerome P.</creator><general>Blackwell Publishing Inc</general><general>Wiley-Blackwell</general><general>Blackwell Publishing Ltd</general><scope>FBQ</scope><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>7X8</scope></search><sort><creationdate>201203</creationdate><title>Modeling Adverse Birth Outcomes via Confirmatory Factor Quantile Regression</title><author>Burgette, Lane F. ; Reiter, Jerome P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4809-dd04a19c733fba502393e2c87211ad32ebf9eb89716caa6a84a76eef4e6e2c493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Bayesian inference</topic><topic>Bayesian networks</topic><topic>BIOMETRIC METHODOLOGY</topic><topic>Biometrics</topic><topic>biometry</topic><topic>Birth Weight</topic><topic>Causality</topic><topic>Female</topic><topic>Fetal Growth Retardation - epidemiology</topic><topic>Gibbs sampling</topic><topic>Humans</topic><topic>Infant, Low Birth Weight</topic><topic>Infant, Newborn</topic><topic>Infant, Very Low Birth Weight</topic><topic>Low birth weight</topic><topic>Maternal Exposure - statistics & numerical data</topic><topic>Mathematical vectors</topic><topic>Modeling</topic><topic>Pregnancy</topic><topic>pregnancy complications</topic><topic>Prevalence</topic><topic>Proportional Hazards Models</topic><topic>Quantile regression</topic><topic>Random variables</topic><topic>Regression Analysis</topic><topic>tobacco</topic><topic>Tobacco Smoke Pollution - statistics & numerical data</topic><topic>Tobacco smoking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Burgette, Lane F.</creatorcontrib><creatorcontrib>Reiter, Jerome P.</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>MEDLINE - Academic</collection><jtitle>Biometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Burgette, Lane F.</au><au>Reiter, Jerome P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling Adverse Birth Outcomes via Confirmatory Factor Quantile Regression</atitle><jtitle>Biometrics</jtitle><addtitle>Biometrics</addtitle><date>2012-03</date><risdate>2012</risdate><volume>68</volume><issue>1</issue><spage>92</spage><epage>100</epage><pages>92-100</pages><issn>0006-341X</issn><eissn>1541-0420</eissn><coden>BIOMA5</coden><abstract>We describe a Bayesian quantile regression model that uses a confirmatory factor structure for part of the design matrix. This model is appropriate when the covariates are indicators of scientifically determined latent factors, and it is these latent factors that analysts seek to include as predictors in the quantile regression. We apply the model to a study of birth weights in which the effects of latent variables representing psychosocial health and actual tobacco usage on the lower quantiles of the response distribution are of interest. The models can be fit using an R package called factorQR.</abstract><cop>Malden, USA</cop><pub>Blackwell Publishing Inc</pub><pmid>21689080</pmid><doi>10.1111/j.1541-0420.2011.01639.x</doi><tpages>9</tpages></addata></record> |
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| ispartof | Biometrics, 2012-03, Vol.68 (1), p.92-100 |
| issn | 0006-341X 1541-0420 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_948909493 |
| source | EBSCOhost SPORTDiscus with Full Text; JSTOR Archival Journals and Primary Sources Collection; Oxford Journals Online |
| subjects | Bayes Theorem Bayesian analysis Bayesian inference Bayesian networks BIOMETRIC METHODOLOGY Biometrics biometry Birth Weight Causality Female Fetal Growth Retardation - epidemiology Gibbs sampling Humans Infant, Low Birth Weight Infant, Newborn Infant, Very Low Birth Weight Low birth weight Maternal Exposure - statistics & numerical data Mathematical vectors Modeling Pregnancy pregnancy complications Prevalence Proportional Hazards Models Quantile regression Random variables Regression Analysis tobacco Tobacco Smoke Pollution - statistics & numerical data Tobacco smoking |
| title | Modeling Adverse Birth Outcomes via Confirmatory Factor Quantile Regression |
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