The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Methods for Analyzing Panel Studies of Acute Health Effects of Air Pollution
Abstract
New methods are presented for analyzing repeated binary health measurements of individuals exposed to varying levels of air pollution. The methods involve a separate logistic regression of response against environmental covariates for each individual. Parameters reflecting individual susceptibility to pollutants and weather are estimated using Cox's regression techniques (1970, 1972a). The individual parameters are combined to yield summary estimates of environmental effects. The approach does not require independence of successive health measurements. It is illustrated with data on asthma and air pollution in the Los Angeles area.
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Cites
EFRON, B., & HINKLEY, D. V. (1978) · Biometrika · doi:10.1093/biomet/65.3.457 · Cited by 952
Cited by
Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
D'Agostino, R. B., Lee, M., Belanger, A. J., Cupples, L. A., Anderson, K., & Kannel, W. B. (1990) · Statistics in Medicine · doi:10.1002/sim.4780091214 · Cited by 798
Random-Effects Models for Serial Observations with Binary Response
Stiratelli, R., Laird, N., & Ware, J. H. (1984) · Biometrics · doi:10.2307/2531147 · Cited by 733
An overview of methods for the analysis of longitudinal data
Zeger, S. L., & Liang, K. (1992) · Statistics in Medicine · doi:10.1002/sim.4780111406 · Cited by 678
Metadata
- Authors
- Korn, E. L., & Whittemore, A. S.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1979-12
- Volume
- 35
- Issue
- 4
- Pages
- 795
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2066886598
- Cited by
- 203
- References
- 1
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
