DOI QR코드

DOI QR Code

Bayesian Modeling of Mortality Rates for Colon Cancer

Kim Hyun-Joong

  • 발행 : 2006.04.01

초록

The aim of this study is to propose a Bayesian model for fitting mortality rate of colon cancer. For the analysis of mortality rate of a disease, factors such as age classes of population and spatial characteristics of the location are very important. The model proposed in this study allows the age class to be a random effect in addition to its conventional role as the covariate of a linear regression, while the spatial factor being a random effect. The model is fitted using Metropolis-Hastings algorithm. Posterior expected predictive deviances, standardized residuals, and residual plots are used for comparison of models. It is found that the proposed model has smaller residuals and better predictive accuracy. Lastly, we described patterns in disease maps for colon cancer.

키워드

Mortality rate;Bayesian modeling;Metropolis-Hastings algorithm;Posterior distribution;Disease mapping

참고문헌

  1. Chib, S. and Greenberg, E. (1995). Understanding the Metropolis-Hastings Algorithm. The American Statistician, Vol. 49, 327-335 https://doi.org/10.2307/2684568
  2. Gelfand, A.E., Sahu, S.K., and Carlin, B.P. (1995). Efficient Parameterizations for Normal Linear Mixed Models. Biometrika, Vol. 82, 479-488 https://doi.org/10.1093/biomet/82.3.479
  3. Gelman, A., Carlin, J.B., Stem, H.S., Rubin, D.B. (1995). Bayesian Data Analysis. Chapman and Hall, London
  4. Nandram, B. and Kim, H. (2002). Marginal likelihood for a class of Bayesian generalized linear models. Journal of Statistical Computation and Simulation, Vol. 72, 319-340 https://doi.org/10.1080/00949650212842
  5. Nandram, B., Sedransk, J. and Pickle, L. (2000). Bayesian analysis and mapping of mortality rates for chronic obstructive pulmonary disease. Journal of the American Statistical Association Vol. 95, 1110-1118 https://doi.org/10.2307/2669747
  6. Gilks, W.R. and Wild, P. (1992). Adaptive Rejection Sampling for Gibbs Sampling. Applied Statistics, Vol. 41, 337-348 https://doi.org/10.2307/2347565
  7. Waller, L., Carlin, B., Xia, H. and Gelfand, A (1997). Hierarchical spatia-temporal mapping of disease rates. Journal of the American Statistical Association, Vol. 92, 607-617 https://doi.org/10.2307/2965708
  8. Nandram, B., Sedransk, J. and Pickle, L. (1999). Bayesian Analysis of Mortality Rates for U.S., Health Service Areas. Sankhya, Series B, Vol. 61, 145-165
  9. Hettmansperger, T. (1984). Statistical Inference Based on Ranks. Wiley, New York
  10. Pickle, L.W., Mungiole, M., Jones, G.K., and White, R. C. (1996). Atlas of us. Mortality. National Center for Health Statistics, Hyattsville, MD
  11. Brillinger, D.R. (1996). The natural variability of vital rates and associated statistics. Biometrics, Vol. 42, 693-734 https://doi.org/10.2307/2530689