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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/30934


    Title: 貝氏時間與空間統計模式之應用
    Authors: 黃佩櫻
    Contributors: 陳麗霞
    黃佩櫻
    Keywords: 階層貝氏
    疾病地圖
    馬可夫鏈蒙地卡羅法
    紅斑性狼瘡
    時空模式
    DIC
    Bayesian
    Spatio-Temporal
    MCMC
    SLE
    disease map
    SMR
    Date: 2004
    Issue Date: 2009-09-14
    Abstract: 本篇論文的目的在介紹階層貝氏之時間與空間統計模式(spatio-temporal model)，將此模式應用在疾病地圖的分析，以了解疾病在空間上的分佈狀態與時間趨勢。模型中除了納入時間、空間和年齡的效應外，也包括時間與空間、時間與年齡的交互作用，並考慮到空間相關性(spatial correlation)，然後以DIC值(Deviance information criterion)作為模式選取的準則。

    本文並以民國88-90年全身紅斑性狼瘡的女性患病人數做為實證分析的資料。配適時間與空間統計模式後，以馬可夫鏈蒙地卡羅法(MCMC)來模擬參數值，估計出各時間、地區、年齡層的對數疾病發生率。由疾病地圖可看出，台灣地區全身紅斑性狼瘡的女性疾病發生率，以20-59歲的年齡層發生率較高，0-19歲的發生率較低。不管在哪一個年齡層，北部和中部地區的發生率都是最高的。時間趨勢方面，88-90年整體疾病發生率有遞減的趨勢，60歲以上的發生率也是遞減的趨勢。但在部分地區，則有發生率遞增的趨勢。
    In this study, we introduce the spatio-temporal model in a hierarchical Bayesian framework and use disease maps to display the spatial patterns and the temporal trends of disease. A special feature of the model is the inclusion of spatial correlations used to examine spatial effects relative to both regional and regional changes over time by group. Then, we use deviance information criterion (DIC) to compare complex hierarchical models.

    The methodology is illustrated by an analysis of female Systemic Lupls Erythematosus (SLE) morbidity data in Taiwan during the period 1999-2001.The model inference is implemented using Markov chain Monte Carlo method. The outcomes of the practical analysis appear that the higher morbidity rate occurs in 20-year and 40-year period. No matter what age group, the morbidity rate is highest in the north and the middle of Taiwan. Furthermore, the morbidity rate decreases with respect to year as well as over the 60-year period but it increases in some places.
    Reference: 1. 張德明：全身性紅斑性狼瘡臨床常見問題的診斷與原則http://www.tsgh.ndmctsgh.edu.tw/ria/sle.htm
    2. 思樂醫之友網頁
    http://www.vghtpe.gov.tw/~air/doc/cha.htm
    3. Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society, Series B, 36, 192-236.
    4. Besag, J., York, J. and Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43,1-59.
    5. Bernardinelli, L.,Clayton, D., Pascutto, C., Montomoli, C., Ghislandi, M. and Songini, M. (1995). Bayesian Analysis of Space-Time Variation in Disease Risk. Statistics in Medicine, 14, 2433-2443.
    6. Burnham, K. P. and Anderson, D. R. (1998). Model Selection and Inference. New York: Springer.
    7. Cressie, Noel A. C. (1993). Statistics for spatial data. New York : John Wiley.
    8. Efron, B. (1986) How biased in the apparent error rate of a prediction rule? Journal of the American Statistical Association, 81, 461-470.
    9. Gilks, W.R., Wild, P. (1992). Adaptive rejection sampling for Gibbs sampling. Applied Statistics, 41, 337-348.
    10. Kullback, S. and Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22, 79-86.
    11. Knorr-Held, L. and Besag, J. (1998). Modeling Risk from a Disease in Time and Space. Statistics in Medicine, 17, 2045-2060.
    12. Mollie, A. (1996). Bayesian mapping of disease. In Markov Chain Monte Carlo in Practice (eds. W. R. Gillks, S. Richardson and D. J. Spiegelhalter), pp.359-379. Chapman& Hall.
    13. MacNab, Y. C. (2003). Hierarchical Bayesian Modeling of Spatially Correlated Health Service Outcome and Utilization Rates. Biometrics, 59, 305-316.
    14. Nobre, A. A., Schmidt , A. M. and Lopes, H. F. (2003). Spatio-temporal models for mapping the Incidence of malaria in Para.
    http://gsbwww.uchicago.edu/fac/hedibert.lopes/research/nobreschmidtlopes2003.pdf
    15. Pickle, L. W. (2000). Exploring spatio-temporl patterns of mortality using mixed effects models. Statistics in Medicine, 19, 2251-2263.
    16. Sun, D., Tsutakawa, R. K., Speckman, P. L.(1999). Bayesian inference for CAR(1) models with noninformative priors. Biometrika, 86,341-350.
    17. Sun, D., Tsutakawa , R. K., Kim, H. and He, Z. (2000). Spatio-temporal interaction with disease mapping. Statistics in Medicine, 19, 2015-2035.
    18. Spiegelhalter, D. J., Best , N. G.., Carlin, B. P. and Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B , 64, 583-639.
    19. Waller, L.A., Carlin, B.P., Xia, H., and Gelfand, A. E. (1997). Hierarchical Spatio-Temporal Mapping of Disease Rates. Journal of the American Statistical Association, 92, 607-617.
    Description: 碩士
    國立政治大學
    統計研究所
    91354013
    93
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0913540131
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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