Statistical methods for hazards and health.
The objective of this article is to document the need for further development of statistical methodology, training of more statisticians and improved communication between statisticians and the many other disciplines engaged in environmental research. Discussion of adequacy of the current statistica...
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| Format: | Online |
| Language: | English |
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1977
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1637341/ |
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pubmed-1637341 |
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pubmed-16373412006-11-17 Statistical methods for hazards and health. Bishop, Y M Research Article The objective of this article is to document the need for further development of statistical methodology, training of more statisticians and improved communication between statisticians and the many other disciplines engaged in environmental research. Discussion of adequacy of the current statistical methodology requires the use of examples, which will hopefully not be offensive to the authors. Reference is made to recent developments and areas of unsolved problems delineated in three broad areas: enumeration data and adjusted rates; time series; and multiple regression. A brief outline of the ideas behind current methods of analyzing discrete data is followed by a demonstration of their utility using an example of the effects of exposure, sex, and education on bronchitis rates. Examples are listed of the ubiquity of the time component when relating pollution effects to each other and to health effects. An artificial example is used to emphasize the effects of time-dependent autocorrelations, trends, and cycles. References are given to a variety of new developments in time-dependent autocorrelations, trends, and cycles. References are given to a variety of new developments in time-series analysis. Discussion of the pitfalls in multiple regression analysis, and possible alternative approaches is largely based on two recent reviews and includes references to recent developments of robust techniques. 1977-10 /pmc/articles/PMC1637341/ /pubmed/598347 Text en |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Bishop, Y M |
| spellingShingle |
Bishop, Y M Statistical methods for hazards and health. |
| author_facet |
Bishop, Y M |
| author_sort |
Bishop, Y M |
| title |
Statistical methods for hazards and health. |
| title_short |
Statistical methods for hazards and health. |
| title_full |
Statistical methods for hazards and health. |
| title_fullStr |
Statistical methods for hazards and health. |
| title_full_unstemmed |
Statistical methods for hazards and health. |
| title_sort |
statistical methods for hazards and health. |
| description |
The objective of this article is to document the need for further development of statistical methodology, training of more statisticians and improved communication between statisticians and the many other disciplines engaged in environmental research. Discussion of adequacy of the current statistical methodology requires the use of examples, which will hopefully not be offensive to the authors. Reference is made to recent developments and areas of unsolved problems delineated in three broad areas: enumeration data and adjusted rates; time series; and multiple regression. A brief outline of the ideas behind current methods of analyzing discrete data is followed by a demonstration of their utility using an example of the effects of exposure, sex, and education on bronchitis rates. Examples are listed of the ubiquity of the time component when relating pollution effects to each other and to health effects. An artificial example is used to emphasize the effects of time-dependent autocorrelations, trends, and cycles. References are given to a variety of new developments in time-dependent autocorrelations, trends, and cycles. References are given to a variety of new developments in time-series analysis. Discussion of the pitfalls in multiple regression analysis, and possible alternative approaches is largely based on two recent reviews and includes references to recent developments of robust techniques. |
| publishDate |
1977 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1637341/ |
| _version_ |
1611390924233900032 |