A statistical approach for establishing tumor incidence delisting criteria in areas of concern

A case study

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The Great Lakes Water Quality Agreement specifies "fish tumors or other deformities" as one of the 14 beneficial use impairments that can be used to declare a geographic area within the Great Lakes an Area of Concern (AOC). The International Joint Commission has suggested that the fish tumor impairment can be delisted when fish tumor incidence in the AOC does not exceed rates at unimpacted control sites. This paper presents a statistical technique utilizing Bayesian hierarchical logistic models to estimate tumor incidence on brown bullheads (Ameiurus nebulosus) in an AOC and in candidate least impacted control sites (LICS). Liver and skin tumor incidence are estimated using age, length, weight, and gender as possible covariates using a hierarchical framework to account for a sampling design in which sites are sampled over multiple years and/or at multiple sublocations within the site. By utilizing a Bayesian approach, estimates of uncertainty for tumor incidence in sites with no observed tumors can be obtained. The posterior distributions of tumor incidence can then be used to identify LICS for the watershed and subsequently compare the tumor incidence in the AOC to the LICS using a Bayesian form of the two one-side tests for equivalence procedure. Presque Isle Bay (Erie, PA) in the Lake Erie watershed is used as a case study to demonstrate the technique.

Original languageEnglish (US)
Pages (from-to)646-655
Number of pages10
JournalJournal of Great Lakes Research
Volume36
Issue number4
DOIs
StatePublished - Dec 1 2010

Fingerprint

tumor
case studies
incidence
neoplasms
Great Lakes
fish
Ameiurus
watershed
Lake Erie
liver neoplasms
logit analysis
lake
lake water
uncertainty
water quality
logistics
skin
gender
methodology
sampling

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Aquatic Science
  • Ecology

Cite this

@article{aaf265d58e9244d4bbe6d8fe03488912,
title = "A statistical approach for establishing tumor incidence delisting criteria in areas of concern: A case study",
abstract = "The Great Lakes Water Quality Agreement specifies {"}fish tumors or other deformities{"} as one of the 14 beneficial use impairments that can be used to declare a geographic area within the Great Lakes an Area of Concern (AOC). The International Joint Commission has suggested that the fish tumor impairment can be delisted when fish tumor incidence in the AOC does not exceed rates at unimpacted control sites. This paper presents a statistical technique utilizing Bayesian hierarchical logistic models to estimate tumor incidence on brown bullheads (Ameiurus nebulosus) in an AOC and in candidate least impacted control sites (LICS). Liver and skin tumor incidence are estimated using age, length, weight, and gender as possible covariates using a hierarchical framework to account for a sampling design in which sites are sampled over multiple years and/or at multiple sublocations within the site. By utilizing a Bayesian approach, estimates of uncertainty for tumor incidence in sites with no observed tumors can be obtained. The posterior distributions of tumor incidence can then be used to identify LICS for the watershed and subsequently compare the tumor incidence in the AOC to the LICS using a Bayesian form of the two one-side tests for equivalence procedure. Presque Isle Bay (Erie, PA) in the Lake Erie watershed is used as a case study to demonstrate the technique.",
author = "Michael Rutter",
year = "2010",
month = "12",
day = "1",
doi = "10.1016/j.jglr.2010.08.008",
language = "English (US)",
volume = "36",
pages = "646--655",
journal = "Journal of Great Lakes Research",
issn = "0380-1330",
publisher = "International Association of Great Lakes Research",
number = "4",

}

TY - JOUR

T1 - A statistical approach for establishing tumor incidence delisting criteria in areas of concern

T2 - A case study

AU - Rutter, Michael

PY - 2010/12/1

Y1 - 2010/12/1

N2 - The Great Lakes Water Quality Agreement specifies "fish tumors or other deformities" as one of the 14 beneficial use impairments that can be used to declare a geographic area within the Great Lakes an Area of Concern (AOC). The International Joint Commission has suggested that the fish tumor impairment can be delisted when fish tumor incidence in the AOC does not exceed rates at unimpacted control sites. This paper presents a statistical technique utilizing Bayesian hierarchical logistic models to estimate tumor incidence on brown bullheads (Ameiurus nebulosus) in an AOC and in candidate least impacted control sites (LICS). Liver and skin tumor incidence are estimated using age, length, weight, and gender as possible covariates using a hierarchical framework to account for a sampling design in which sites are sampled over multiple years and/or at multiple sublocations within the site. By utilizing a Bayesian approach, estimates of uncertainty for tumor incidence in sites with no observed tumors can be obtained. The posterior distributions of tumor incidence can then be used to identify LICS for the watershed and subsequently compare the tumor incidence in the AOC to the LICS using a Bayesian form of the two one-side tests for equivalence procedure. Presque Isle Bay (Erie, PA) in the Lake Erie watershed is used as a case study to demonstrate the technique.

AB - The Great Lakes Water Quality Agreement specifies "fish tumors or other deformities" as one of the 14 beneficial use impairments that can be used to declare a geographic area within the Great Lakes an Area of Concern (AOC). The International Joint Commission has suggested that the fish tumor impairment can be delisted when fish tumor incidence in the AOC does not exceed rates at unimpacted control sites. This paper presents a statistical technique utilizing Bayesian hierarchical logistic models to estimate tumor incidence on brown bullheads (Ameiurus nebulosus) in an AOC and in candidate least impacted control sites (LICS). Liver and skin tumor incidence are estimated using age, length, weight, and gender as possible covariates using a hierarchical framework to account for a sampling design in which sites are sampled over multiple years and/or at multiple sublocations within the site. By utilizing a Bayesian approach, estimates of uncertainty for tumor incidence in sites with no observed tumors can be obtained. The posterior distributions of tumor incidence can then be used to identify LICS for the watershed and subsequently compare the tumor incidence in the AOC to the LICS using a Bayesian form of the two one-side tests for equivalence procedure. Presque Isle Bay (Erie, PA) in the Lake Erie watershed is used as a case study to demonstrate the technique.

UR - http://www.scopus.com/inward/record.url?scp=78649446687&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649446687&partnerID=8YFLogxK

U2 - 10.1016/j.jglr.2010.08.008

DO - 10.1016/j.jglr.2010.08.008

M3 - Article

VL - 36

SP - 646

EP - 655

JO - Journal of Great Lakes Research

JF - Journal of Great Lakes Research

SN - 0380-1330

IS - 4

ER -