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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/30922
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/30922


    Title: 空間相關存活資料之貝氏半參數比例勝算模式
    Bayesian semiparametric proportional odds models for spatially correlated survival data
    Authors: 張凱嵐
    Chang, Kai lan
    Contributors: 陳麗霞
    張凱嵐
    Chang, Kai lan
    Keywords: 空間聚集
    比例勝算模型
    貝氏階層模型
    混合Polya樹
    馬可夫鏈蒙地卡羅模擬
    多變量條件自回歸模型
    條件預測指標
    平均對數擬邊際概似函數值
    離差訊息準則
    spatial clusters
    proportional odds
    Bayesian hierarchical model
    mixture of Polya trees
    Markov Chain Monte Carlo (MCMC)
    multivariate conditionally autoregressive (MCAR)
    average log-marginal pseudo-likelihood (ALMPL)
    conditional predictive ordinate (CPO)
    deviance information criterion (DIC)
    Date: 2008
    Issue Date: 2009-09-14
    Abstract: 近來地理資訊系統(GIS)之資料庫受到不同領域的統計學家廣泛的研究,以期建立及分析可描述空間聚集效應及變異之模型,而描述空間相關存活資料之統計模式為公共衛生及流行病學上新興的研究議題。本文擬建立多維度半參數的貝氏階層模型,並結合空間及非空間隨機效應以描述存活資料中的空間變異。此模式將利用多變量條件自回歸(MCAR)模型以檢驗在不同地理區域中是否存有空間聚集效應。而基準風險函數之生成為分析貝氏半參數階層模型的重要步驟,本研究將利用混合Polya樹之方式生成基準風險函數。美國國家癌症研究院之「流行病監測及最終結果」(Surveillance Epidemiology and End Results, SEER)資料庫為目前美國最完整的癌症病人長期追蹤資料,包含癌症病人存活狀況、多重癌症史、居住地區及其他分析所需之個人資料。本文將自此資料庫擷取美國愛荷華州之癌症病人資料為例作實證分析,並以貝氏統計分析中常用之模型比較標準如條件預測指標(CPO)、平均對數擬邊際概似函數值(ALMPL)、離差訊息準則(DIC)分別測試其可靠度。
    The databases of Geographic Information System (GIS) have gained attention among different fields of statisticians to develop and analyze models which account for spatial clustering and variation. There is an emerging interest in modeling spatially correlated survival data in public health and epidemiologic studies. In this article, we develop Bayesian multivariate semiparametric hierarchical models to incorporate both spatially correlated and uncorrelated frailties to answer the question of spatial variation in the survival patterns, and we use multivariate conditionally autoregressive (MCAR) model to detect that whether there exists the spatial cluster across different areas. The baseline hazard function will be modeled semiparametrically using mixtures of finite Polya trees. The SEER (Surveillance Epidemiology and End Results) database from the National Cancer Institute (NCI) provides comprehensive cancer data about patient’s survival time, regional information, and others demographic information. We implement our Bayesian hierarchical spatial models on Iowa cancer data extracted from SEER database. We illustrate how to compute the conditional predictive ordinate (CPO), the average log-marginal pseudo-likelihood (ALMPL), and deviance information criterion (DIC), which are Bayesian criterions for model checking and comparison among competing models.
    Reference: Aslanidou, H., Dey, D.K. and Sinha, D. (1998). Bayesian analysis of multivariate survival data using Monte Carlo methods. Canadian Journal of Statistics, 26, 33-48.
    Banerjee, S, Carlin, B.P. and Gelfand, A.E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Boca Raton: Chapman and Hall/CRC.
    Banerjee, S., Wall, M. and Carlin, B.P. (2003). Frailty modelling for spatially correlated survival data with application to infant mortality in Minnesota. Biostatistics, 4, 123–142.
    Banerjee, S. and Dey, D.K. (2005). Semiparametric proportional odds model for spatially correlated survival data. Lifetime Data Analysis, 11, 175–191.
    Besag, J. (1974). Spatial Interaction and the Statistical Analysis of Lattice Systems (with Discussion). Journal of the Royal Statistical Society, Ser. B, 36, 192–236.
    Bennett, S. (1983). Analysis of survival data by the proportional odds model. Statistics in Medicine, 2, 273–277.
    Brook, D. (1964). On the distinction between the conditional probability and the joint probability approaches in the specification of nearest-neighbour systems. Biometrika, 51(3-4), 481-483
    Carlin, B.P. and Banerjee, S. (2003). Hierarchical multivariate car models for spatio-temporally correlated survival data. Bayesian Statistics, 7, 45–64.
    Celeux, G., Forbes, F., Robert, C.P. and Titterington, D.M. (2006). Deviance information criteria for missing data models (with discussion). Bayesian Analysis, 1, 651–706.
    Clayton, D. G. (1978). A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika, 65,141–151.
    Cox, D.R. (1972). Regression models with life tables. Journal of the Royal Statistical Society, 34, 187–220.
    Diva, U.A., Banerjee, S. and Dey, D.K. (2007). Modeling spatially correlated survival data for individuals with multiple cancers. Statistical Modeling, 7(2), 1–23.
    Diva, U.A., Dey, D.K. and Banerjee, S. (2008). Parametric models for spatially correlated survival data for individuals with multiple cancers. Statistics in Medicine, 27, 2127–2144.
    Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1, 209–230.
    Gelfand, A.E. and Vounatsou, P. (2002). Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics, 4, 11–25.
    Gilks, W.R. and Wild, P. (1992). Adaptive Rejection Sampling for Gibbs Sampling. Applied Statistics, 41(2), 337-348
    Geisser, S. and Eddy, W.F. (1979). A predictive approach to model selection. Journal of the American Statistical Association, 74, 153-160.
    Hammersley, J.M. and Clifford, P. (1971). Markov fields on finite graphs and lattices. Unpublished.
    Kelderman, H. (1984). Loglinear Rasch model tests. Psychometrika, 49, 223–45.
    Kraft, C.H. (1964). A Class of Distribution Function Processes Which Have Derivatives. Journal of Applied Probability, 1, 385-388
    Han, C. and Carlin, B.P. (2001). Markov chain Monte Carlo methods for computing Bayes factors: a comparative review. Journal of the American Statistical Association, 96, 1122-1132.
    Hanson, T. and Johnson, W.O. (2002). Modeling regression error with a mixture of Polya trees. Journal of the American Statistical Association, 97, 1020–1033.
    Hanson, T. (2006). Inference for mixtures of finite Polya tree models. Journal of the American Statistical Association, 101, 1548-1565.
    Hanson, T. and Yang, M. (2007). Bayesian semiparametric proportional odds models. Biometrics, 63, 88-95.
    Heinävaara, S. (2003). Modelling survival of patients with multiple cancers. Ph.D. Thesis, University of Helsinki, Statistical Research Reports, No. 18. The Finnish Statistical Society.
    Held, L. and Best, N.G. (2001). A shared component model for detecting joint and selective clustering of two diseases. Journal of the Royal Statistical Society, Series A, 164, 73–85.
    Held, L., Natario, I., Fenton, S., Rue, H. and Becker, N. (2005). Towards joint disease mapping. Statistical Methods in Medical Research, 14, 61–82.
    Ibrahim, J.G., Chen, M.H. and Sinha, D. (2001). Bayesian Survival Analysis, New York: Springer-Verlag.
    Jin, X. and Carlin, B.P. (2005). Multivariate parametric spatio-temporal models for county level breast cancer survival data. Lifetime Data Analysis, 11, 5-27.
    Jin, X., Carlin, B.P. and Banerjee, S. (2005). Generalized hierarchical multivariate car models for areal data. Biometrics, 61, 950–961.
    Lam, K. F., Lee, Y. W., and Leung, T. L. (2002). Modeling multivariate survival data by a semiparametric random effects proportional odds model. Biometrics, 58, 316–323.
    Lavine, M. (1992). Some aspects of Polya tree distributions for statistical modeling. Annals of Statistics, 20, 1222–1235.
    Lichstein, J.W., Simons, T.R., Shriner, S.A. and Franzreb. K.E. (2002). Spatial autocorrelation and autoregressive models in ecology. Ecological Monographs, 72(3), 445–463.
    Mallick, B.K. and Walker, S.G. (2003). A Bayesian semiparametric transformation model incorporating frailties. Journal of Statistical Planning and Inference, 112, 159-174.
    Mardia, K. V. (1988). Multi-Dimensional Multivariate Gaussian Markov Random Fields with Application to Image Processing. Journal of Multivariate Analysis, 24, 265–284.
    Murphy, S. A., Rossini, A. J., and van der Vaart, A. W. (1997). Maximum likelihood estimation in the proportional odds model. Journal of the American Statistical Association, 92, 968–976.
    National Cancer Institute (NCI). Cancer Facts: Cancer Clusters, Fact Sheet. http://cancertrials.nci.nih.gov/images/Documents/
    Ries, L.A.G., Eisner, M.P., Kosary, C.L., Hankey, B.F., Miller, B.A., Clegg, L., Mariotto, A., Feuer, E.J. and Edwards, B.K. (eds). SEER Cancer Statistics Review, 1975–2002, National Cancer Institute, Bethesda, MD. Available from: http://seer.cancer.gov/csr/1975 2002/, based on November 2004 SEER data submission, posted to the SEER Web site 2005
    Sahu, S. K., Dey, D. K., Aslanidou, H., and Sinha, D. (1997). A Weibull regression model with gamma frailties for multivariate survival data. Lifetime Data Analysis, 3, 123–137.
    Sinha, D. and Dey, D. K. (1997). Semiparametric Bayesian analysis of survival data. Journal of the American Statistical Association, 92, 1195–1212.
    Spiegelhalter, D.J., Best, N., Carlin, B.P., and van der Linde, (2002). A. Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B, 64, 583-639.
    Sun, D., Tsutakawa, R.K., Kim, H., and Zhuoqiong, H. (2000). Spatio-temporal interaction with disease mapping. Statistics in Medicine, 19, 2015-2035.
    Sundaram, S. (2006). Semiparametric inference in proportional odds model with time-dependent covariates. Journal of Statistical Planning and Inference, 136, 320–334.
    Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) SEER*Stat Database: Incidence—SEER 11 Regs + AK Public-use, November 2005 Sub (1973–2005 varying). National Cancer Institute, DCCPS, Surveillance Research Program, Cancer Statistics Branch, released April 2004, based on the November 2007 submission
    Walker, S.G. and Mallick, B.K. (1997). Hierarchical generalized linear models and frailty models with Bayesian nonparametric mixing. Journal of the Royal Statistical Society, Series B, 59, 845-860.
    Walker, S.G. and Mallick, B.K. (1999). Semiparametric accelerated life time model. Biometrics, 55, 477-483.
    Yang, S. and Prentice, R.L. (1999). Semiparametric inference in the proportional odds regression model. Journal of the American Statistical Association, 94, 125–136.
    Zhao, L., Hanson, T., and Carlin, B.P. (2009). Mixtures of Polya trees for flexible spatial frailty survival modeling. Biometrika, 96(2), 263–276
    Zucker, D.M. and Yang, S. (2005). Inference for a family of survival models encompassing the proportional hazards and proportional odds model. Statistics in Medicine, 25, 995–1014.
    Description: 碩士
    國立政治大學
    統計研究所
    95354013
    97
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0095354013
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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