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題名 貝氏時間與空間統計模式之應用
作者 黃佩櫻
貢獻者 陳麗霞
黃佩櫻
關鍵詞 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
日期 2004
上傳時間 2009-09-14
摘要 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.
參考文獻 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.
描述 碩士
國立政治大學
統計研究所
91354013
93
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0913540131
資料類型 thesis
dc.contributor.advisor 陳麗霞zh_TW
dc.contributor.author (Authors) 黃佩櫻zh_TW
dc.creator (作者) 黃佩櫻zh_TW
dc.date (日期) 2004en_US
dc.date.accessioned 2009-09-14-
dc.date.available 2009-09-14-
dc.date.issued (上傳時間) 2009-09-14-
dc.identifier (Other Identifiers) G0913540131en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/30934-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 91354013zh_TW
dc.description (描述) 93zh_TW
dc.description.abstract (摘要) 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。
zh_TW
dc.description.abstract (摘要) 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.
en_US
dc.description.tableofcontents 第一章 緒論…………………………………………………………………… 1
     第一節 研究動機與目的………………………………………………… 1
     第二節 研究背景………………………………………………………… 2
     第三節 研究架構………………………………………………………… 4
     第二章 文獻探討……………………………………………………………… 5
      第一節 CAR模式………………………………………………………… 5
      第二節 時間與空間統計模式…………………………………………… 7
     第三章 研究方法……………………………………………………………… 9
      第一節 模式介紹………………………………………………………… 9
      一 和 的分配…………………………………………………... 10
      二 參數的先驗分配………………………………………………… 10
      三 聯合後驗分配…………………………………………………… 11
      第二節 馬可夫鏈蒙地卡羅法(MCMC)模擬參數值……………………. 12
      一 MCMC法與吉氏抽樣法(Gibbs sampling)之介紹……………... 12
      二 MCMC法估計參數值…………………………………………... 14
      第三節 以DIC(Deviance Information Criterion)進行模式選取………... 20
      一 及 ………………………………………………………. 21
      二 DIC與後驗分配…………………………………………………. 21
     第四章 實證研究……………………………………………………………… 23
      第一節 配適模式………………………………………………………… 23
      第二節 空間相關性之討論與模式選取………………………………… 24
      第三節 模式分析與疾病地圖…………………………………………… 27
      一 年齡效應 與 之分析………………………………………... 27
      二 時間效應 之分析……...….…………………………………... 29
      三 地區效應 與 之分析……………………………………….. 30
      四 對數疾病發生率 之分析.………………………………... 32
      五 額外變異 之分析…………………………………………….. 41
     第四節 結論……..……………………………………………………….. 43
     第五章 結論與建議……..…………………………………………………….. 44
      第一節 結論……………………………………………………………… 44
      第二節 後續研究及建議………………………………………………… 45
     參考文獻……..………………………………………………………………… 46
     附錄一……..…………………………………………………………………… 48
     附錄二……..…………………………………………………………………… 61
     附錄三……..…………………………………………………………………… 65
     
     
     表 目 錄
     表1.1 88-90年全身紅斑性狼瘡患病人數………………………………… 2
     表1.2 88-90年全身紅斑性狼瘡各年齡層患病人數……………………… 3
     表1.3 88-90年全身紅斑性狼瘡各年齡層女性就診率(%)...……………... 3
     表1.4 全身紅斑性狼瘡患者排名前5名的死因統計……………………... 4
     表4.1 五種模式的DIC值………………………………………………….. 26
     表4.2 與 的估計值……………………………………………………. 28
     表4.3 的估計值…………………………………………………………. 29
     表4.4 在各年齡層中三年疾病發生率皆為全部鄉鎮市的前10%的地區.. 33
     表4.5 各年度所有年齡層之疾病發生率皆在前10%的鄉鎮市………….. 33
     表4.6 連續二年疾病發生率都增加的地區個數………………………….. 34
     表4.7 各年齡層三年的 值皆為前10%的地區…………………………. 41
     
     
     
     
     
     
     
     
     
     圖 目 錄
     圖4.1 與 的後驗分配…….…………………………………………….. 25
     圖4.2 與 對 的效應…………………………………………… 25
     圖4.3 完整模式與簡化模式的 與 之散佈圖…………………………… 26
     圖4.4 與 的後驗分配…………………………………………………... 28
     圖4.5 的後驗分配………………………………………………………… 29
     圖4.6 , 與 的後驗分配………………………………………………... 30
     圖4.7 地區效應 的後驗平均之分佈地圖………………………………… 31
     圖4.8 地區與時間交互作用 的後驗平均之分佈地圖…………………… 31
     圖4.9 各年與各年齡層標準化疾病發生率(SMR)之分佈地圖.………….. 35
     圖4.10 各年與各年齡層估計的疾病發生率 之分佈地圖………….. 38
     圖4.11 各年及各年齡層 的後驗平均之分佈地圖……………………… 42
zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0913540131en_US
dc.subject (關鍵詞) 階層貝氏zh_TW
dc.subject (關鍵詞) 疾病地圖zh_TW
dc.subject (關鍵詞) 馬可夫鏈蒙地卡羅法zh_TW
dc.subject (關鍵詞) 紅斑性狼瘡zh_TW
dc.subject (關鍵詞) 時空模式zh_TW
dc.subject (關鍵詞) DICen_US
dc.subject (關鍵詞) Bayesianen_US
dc.subject (關鍵詞) Spatio-Temporalen_US
dc.subject (關鍵詞) MCMCen_US
dc.subject (關鍵詞) SLEen_US
dc.subject (關鍵詞) disease mapen_US
dc.subject (關鍵詞) SMRen_US
dc.title (題名) 貝氏時間與空間統計模式之應用zh_TW
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 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.htmzh_TW
dc.relation.reference (參考文獻) 2. 思樂醫之友網頁zh_TW
dc.relation.reference (參考文獻) http://www.vghtpe.gov.tw/~air/doc/cha.htmzh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 6. Burnham, K. P. and Anderson, D. R. (1998). Model Selection and Inference. New York: Springer.zh_TW
dc.relation.reference (參考文獻) 7. Cressie, Noel A. C. (1993). Statistics for spatial data. New York : John Wiley.zh_TW
dc.relation.reference (參考文獻) 8. Efron, B. (1986) How biased in the apparent error rate of a prediction rule? Journal of the American Statistical Association, 81, 461-470.zh_TW
dc.relation.reference (參考文獻) 9. Gilks, W.R., Wild, P. (1992). Adaptive rejection sampling for Gibbs sampling. Applied Statistics, 41, 337-348.zh_TW
dc.relation.reference (參考文獻) 10. Kullback, S. and Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22, 79-86.zh_TW
dc.relation.reference (參考文獻) 11. Knorr-Held, L. and Besag, J. (1998). Modeling Risk from a Disease in Time and Space. Statistics in Medicine, 17, 2045-2060.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 13. MacNab, Y. C. (2003). Hierarchical Bayesian Modeling of Spatially Correlated Health Service Outcome and Utilization Rates. Biometrics, 59, 305-316.zh_TW
dc.relation.reference (參考文獻) 14. Nobre, A. A., Schmidt , A. M. and Lopes, H. F. (2003). Spatio-temporal models for mapping the Incidence of malaria in Para.zh_TW
dc.relation.reference (參考文獻) http://gsbwww.uchicago.edu/fac/hedibert.lopes/research/nobreschmidtlopes2003.pdfzh_TW
dc.relation.reference (參考文獻) 15. Pickle, L. W. (2000). Exploring spatio-temporl patterns of mortality using mixed effects models. Statistics in Medicine, 19, 2251-2263.zh_TW
dc.relation.reference (參考文獻) 16. Sun, D., Tsutakawa, R. K., Speckman, P. L.(1999). Bayesian inference for CAR(1) models with noninformative priors. Biometrika, 86,341-350.zh_TW
dc.relation.reference (參考文獻) 17. Sun, D., Tsutakawa , R. K., Kim, H. and He, Z. (2000). Spatio-temporal interaction with disease mapping. Statistics in Medicine, 19, 2015-2035.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW
dc.relation.reference (參考文獻) 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.zh_TW