Hierarchical Bayesian Modeling of Spatially Correlated Health Service Outcome and Utilization Rates

Biometrics - Tập 59 Số 2 - Trang 305-315 - 2003
Ying C. MacNab1
1Division of Biostatistics and Epidemiology, Department of Health Care and Epidemiology, University of British Columbia, Vancouver, B.C., Canada V6H 3V4. [email protected]

Tóm tắt

SummaryWe present Bayesian hierarchical spatial models for spatially correlated small‐area health service outcome and utilization rates, with a particular emphasis on the estimation of both measured and unmeasured or unknown covariate effects. This Bayesian hierarchical model framework enables simultaneous modeling of fixed covariate effects and random residual effects. The random effects are modeled via Bayesian prior specifications reflecting spatial heterogeneity globally and relative homogeneity among neighboring areas. The model inference is implemented using Markov chain Monte Carlo methods. Specifically, a hybrid Markov chain Monte Carlo algorithm (Neal, 1995,Bayesian Learning for Neural Networks;Gustafson, MacNab, and Wen, 2003,Statistics and Computing,to appear) is used for posterior sampling of the random effects. To illustrate relevant problems, methods, and techniques, we present an analysis of regional variation in intraventricular hemorrhage incidence rates among neonatal intensive care unit patients across Canada.

Từ khóa


Tài liệu tham khảo

Besag J., 1974, Spatial interaction and the statistical analysis of lattice systems (with discussion), Journal of the Royal Statistical Society, Series B, 36, 192

Besag J., 1995, On conditional and intrinsic autoregression, Biometrika, 82, 733

10.1007/BF00116466

Best N. G., 1999, Disease Mapping and Risk Assessment for Public Health, 194

Best N. G., 1999, Bayesian Statistics 6, 131, 10.1093/oso/9780198504856.003.0006

10.1201/9781420057669

10.1093/ije/22.6.1193

10.1136/adc.66.4_Spec_No.403

10.1016/0370-2693(87)91197-X

10.1002/1097-0258(20000915/30)19:17/18<2279::AID-SIM569>3.0.CO;2-R

Elliott P., 1992, Geographical and Environmental Epidemiology: Methods for Small‐Area Studies

Gatsonis C. A., 1995, Variations in the utilization of coronary angiography for elderly patients with an acute myocardial infarction: An analysis using hierarchical logistic regression, Health Care, 33, 625

Gelfand A. E., 2001, On computation using Gibbs sampling for multilevel models, Statistica Sinica, 11, 981

Gelfand A. E., 1992, Bayesian Statistics 4, 147, 10.1093/oso/9780198522669.003.0009

10.1214/ss/1177011136

10.1097/00005650-198108000-00006

10.2307/2533110

Gustafson P., 2003, The value of derivatives and random walk suppression in Markov chain Monte Carlo algorithms, Statistics and Computing,

10.1093/biomet/57.1.97

10.1016/0370-2693(91)90812-5

10.2307/1390827

Kelsall J. E., 1998, Modeling spatial variation in disease risk

Lawson A., 1999, Disease Mapping and Risk Assessment for Public Health

10.1542/peds.106.5.1070

Leroux B. G., 1999, Statistical Models in Epidemiology, the Environment and Clinical Trials, 135

MacNab Y. C., 2003, Accident Analysis and Prevention,

10.1002/sim.1463

10.1002/1097-0258(20000915/30)19:17/18<2421::AID-SIM579>3.0.CO;2-C

10.1111/j.0006-341X.2001.00949.x

MacNab Y. C., 2002, Bayesian estimation in random effects spatial and spatio‐temporal models.

10.1063/1.1699114

Neal R. M., 1993, Technical Report CRAG‐TR‐93‐1, Probabilistic inference using Markov Chain Monte Carlo methods

Neal R. M., 1995, Bayesian Learning for Neural Networks

10.1111/1467-9868.00353

10.1067/mpd.2001.111822

Vermont‐Oxford Trials Network, 1993, The Vermont‐Oxford trials network: Very low birth weight outcomes for 1990, Pediatrics, 91, 540, 10.1542/peds.91.3.540

Wakefield J., 1999, Bayesian Statistics 6, 657, 10.1093/oso/9780198504856.003.0029

10.1542/peds.59.6.821

Thông tin
Thông tin xuất bản

Biometrics

Tập 59 Số 2

305-315

Thông tin tác giả