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A graphical analysis of the interrelationships among waterborne asbestos, digestive system cancer and population density.
Five statistical procedures were used to partial the correlation between waterborne asbestos and digestive site cancer for the putative effects of population density. These include: analysis based on a data subset with roughly homogeneous population density; standard residual analysis (partial corre...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
1983
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569086/ https://www.ncbi.nlm.nih.gov/pubmed/6662097 |
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author | Tarter, M E Cooper, R C Freeman, W R |
author_facet | Tarter, M E Cooper, R C Freeman, W R |
author_sort | Tarter, M E |
collection | PubMed |
description | Five statistical procedures were used to partial the correlation between waterborne asbestos and digestive site cancer for the putative effects of population density. These include: analysis based on a data subset with roughly homogeneous population density; standard residual analysis (partial correlation); conditional probability integral transformation; analysis based upon ranked data, and use of logarithmic transformation. Nonparametric regression graphical techniques are applied to examine the nature or shape of the asbestos-cancer dose-response curve. Evidence is presented that suggests that there is considerable difference between analyses involving nonhigh-density tracts and non-San Francisco tracts. Evidence is also presented that the modal-type nonparametric regression curve forks or bifurcates when adjustment is made for population density. |
format | Text |
id | pubmed-1569086 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 1983 |
record_format | MEDLINE/PubMed |
spelling | pubmed-15690862006-09-18 A graphical analysis of the interrelationships among waterborne asbestos, digestive system cancer and population density. Tarter, M E Cooper, R C Freeman, W R Environ Health Perspect Research Article Five statistical procedures were used to partial the correlation between waterborne asbestos and digestive site cancer for the putative effects of population density. These include: analysis based on a data subset with roughly homogeneous population density; standard residual analysis (partial correlation); conditional probability integral transformation; analysis based upon ranked data, and use of logarithmic transformation. Nonparametric regression graphical techniques are applied to examine the nature or shape of the asbestos-cancer dose-response curve. Evidence is presented that suggests that there is considerable difference between analyses involving nonhigh-density tracts and non-San Francisco tracts. Evidence is also presented that the modal-type nonparametric regression curve forks or bifurcates when adjustment is made for population density. 1983-11 /pmc/articles/PMC1569086/ /pubmed/6662097 Text en |
spellingShingle | Research Article Tarter, M E Cooper, R C Freeman, W R A graphical analysis of the interrelationships among waterborne asbestos, digestive system cancer and population density. |
title | A graphical analysis of the interrelationships among waterborne asbestos, digestive system cancer and population density. |
title_full | A graphical analysis of the interrelationships among waterborne asbestos, digestive system cancer and population density. |
title_fullStr | A graphical analysis of the interrelationships among waterborne asbestos, digestive system cancer and population density. |
title_full_unstemmed | A graphical analysis of the interrelationships among waterborne asbestos, digestive system cancer and population density. |
title_short | A graphical analysis of the interrelationships among waterborne asbestos, digestive system cancer and population density. |
title_sort | graphical analysis of the interrelationships among waterborne asbestos, digestive system cancer and population density. |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569086/ https://www.ncbi.nlm.nih.gov/pubmed/6662097 |
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