The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Age- and Tumor Subtype–Specific Breast Cancer Risk Estimates for CH EK 2 *1100delC Carriers
Schmidt, M. K., Hogervorst, F., van Hien, R., Cornelissen, S., Broeks, A., Adank, M. A., Meijers, H., Waisfisz, Q., Hollestelle, A., Schutte, M., et al. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2016.66.5844 · Cited by 173
Auchincloss, A. H., Diez Roux, A. V., Brown, D. G., Erdmann, C. A., & Bertoni, A. G. (2008) · Epidemiology · doi:10.1097/ede.0b013e31815c480 · Cited by 167
Policing and risk of overdose mortality in urban neighborhoods
Bohnert, A. S. B., Nandi, A., Tracy, M., Cerdá, M., Tardiff, K. J., Vlahov, D., & Galea, S. (2011) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2010.07.008 · Cited by 122
McMain, S. F., Chapman, A. L., Kuo, J. R., Guimond, T., Streiner, D. L., Dixon-Gordon, K. L., Isaranuwatchai, W., & Hoch, J. S. (2018) · BMC Psychiatry · doi:10.1186/s12888-018-1802-z · Cited by 82
On the sensitivity of bayes factors to the prior distributions
Sinharay, S., & Stern, H. S. (2002) · The American Statistician · doi:10.1198/000313002137 · Cited by 72
Liu, K.-Y. (2017) · Social Problems · doi:10.1093/socpro/spw044 · Cited by 9
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.