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Empirical Bayes Estimates of Age-Standardized Relative Risks for Use in Disease Mapping
Abstract
There have been many attempts in recent years to map incidence and mortality from diseases such as cancer. Such maps usually display either relative rates in each district, as measured by a standardized mortality ratio (SMR) or some similar index, or the statistical significance level for a test of the difference between the rates in that district and elsewhere. Neither of these approaches is fully satisfactory and we propose a new approach using empirical Bayes estimation. The resulting estimators represent a weighted compromise between the SMR, the overall mean relative rate, and a local mean of the relative rate in nearby areas. The compromise solution depends on the reliability of each individual SMR and on estimates of the overall amount of dispersion of relative rates over different districts.
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Citations
Cites
Nonparametric maximum likelihood estimation of a mixing distribution
Laird, N. (1978) · Journal of the American Statistical Association · doi:10.1080/01621459.1978.10480103 · Cited by 753
Cited by
Bayesian Measures of Model Complexity and Fit
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00353 · Cited by 151
League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance
Goldstein, H., & Spiegelhalter, D. J. (1996) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2983325 · Cited by 1,069
On conditional and intrinsic autoregressions
BESAG, J., & KOOPERBERG, C. (1995) · Biometrika · doi:10.1093/biomet/82.4.733 · Cited by 732
Randomization, Statistics, and Causal Inference
Greenland, S. (1990) · Epidemiology · doi:10.1097/00001648-199011000-00003 · Cited by 385
Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan
Morris, M., Wheeler-Martin, K., Simpson, D., Mooney, S. J., Gelman, A., & DiMaggio, C. (2019) · Spatial and Spatio-temporal Epidemiology · doi:10.1016/j.sste.2019.100301 · Cited by 192
Metadata
- Authors
- Clayton, D., & Kaldor, J.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1987-09
- Volume
- 43
- Issue
- 3
- Pages
- 671
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2170712385
- Cited by
- 1,268
- References
- 1
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- closed
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