A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data
Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these so...
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| Published in | International journal of environmental research and public health Vol. 12; no. 9; pp. 10648 - 10661 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
28.08.2015
MDPI |
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| Online Access | Get full text |
| ISSN | 1660-4601 1661-7827 1660-4601 |
| DOI | 10.3390/ijerph120910648 |
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| Abstract | Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data, but incorporate sensitivity analyses into part of the total data analysis. We extend previous work on Poisson regression models that allow for misclassification by thoroughly discussing the basis for the models and allowing for extra-Poisson variability in the form of random effects. Via simulation we show the improvements in inference that are brought about by accounting for both the misclassification and the overdispersion. |
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| AbstractList | Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data, but incorporate sensitivity analyses into part of the total data analysis. We extend previous work on Poisson regression models that allow for misclassification by thoroughly discussing the basis for the models and allowing for extra-Poisson variability in the form of random effects. Via simulation we show the improvements in inference that are brought about by accounting for both the misclassification and the overdispersion. |
| Author | Wu, Wenqi Stamey, James Kahle, David |
| AuthorAffiliation | Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX, 76706, USA; E-Mails: wenqi_wu@baylor.edu (W.W.); james_stamey@baylor.edu (J.S.) |
| AuthorAffiliation_xml | – name: Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX, 76706, USA; E-Mails: wenqi_wu@baylor.edu (W.W.); james_stamey@baylor.edu (J.S.) |
| Author_xml | – sequence: 1 givenname: Wenqi surname: Wu fullname: Wu, Wenqi – sequence: 2 givenname: James surname: Stamey fullname: Stamey, James – sequence: 3 givenname: David surname: Kahle fullname: Kahle, David |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26343704$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1214/06-BA117A 10.1016/j.csda.2004.07.004 10.1093/aje/kwt309 10.2307/2532315 10.1093/aje/kwu207 10.1093/aje/kws340 10.1201/9781420010138 10.3390/ijerph111010951 10.1093/aje/kwh211 10.1111/j.1541-0420.2005.00396.x 10.1002/sim.3134 10.1093/ije/dys141 10.3390/ijerph110909553 10.1016/j.canep.2012.11.006 10.1093/ije/dyu148 10.1016/j.canep.2012.08.012 |
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| Keywords | overdispersion count data misclassification |
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| Title | A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data |
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