Abstract
Partitioned regression analysis compared to case-crossover analysis
Epidemiology, Vol.15(5), p.S155
Conference of the International Society for Environmental Epidemiology (ISEE), 16th (New York, United States, 01-Aug-2004–04-Aug-2004)
2004
Abstract
Case-crossover analyses have proved a useful method for studying the acute effects of air pollution whilst controlling for season and temperature. The only drawback has been the choice of the set of control days, which is complicated by the need to balance between overmatching and the adequate controlling of confounders. We overcame the need for control selection by using a partitioned regression of time-matched differences in mortalities against the associated differences in exposures and confounders. The advantages of taking such differences are: the co-linearity between exposures is generally reduced, as is the autocorrelation within exposures; and the outcome variable can be characterised using a Normal distribution.
Details
- Title
- Partitioned regression analysis compared to case-crossover analysis
- Authors
- A Barnett (Author) - University of QueenslandAnne H Neller (Author) - University of the Sunshine Coast - Faculty of Science, Health and EducationTrudi Best (Author) - University of the Sunshine Coast - Faculty of Science, Health and EducationRodney W Simpson (Author) - University of the Sunshine Coast - Faculty of Science, Health and EducationGail M Williams (Author) - University of Queensland
- Contributors
- Allen J Wilcox (Editor)
- Publication details
- Epidemiology, Vol.15(5), p.S155
- Conference details
- Conference of the International Society for Environmental Epidemiology (ISEE), 16th (New York, United States, 01-Aug-2004–04-Aug-2004)
- Publisher
- Lippincott Williams & Wilkins
- Date published
- 2004
- ISSN
- 1044-3983
- Organisation Unit
- Insights & Analytics Unit; University of the Sunshine Coast, Queensland; Strategic Information and Analysis Unit - Legacy; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99447772802621
- Output Type
- Abstract
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