請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3972
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張淑惠 | |
dc.contributor.author | Deng-Huang Su | en |
dc.contributor.author | 蘇登煌 | zh_TW |
dc.date.accessioned | 2021-05-13T08:39:30Z | - |
dc.date.available | 2018-02-24 | |
dc.date.available | 2021-05-13T08:39:30Z | - |
dc.date.copyright | 2016-02-24 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-02-15 | |
dc.identifier.citation | Al-Khalidi, H. R., Hong, Y., Fleming, T. R., and Therneau, T. M. (2011). Insights on the robust variance estimator under recurrent-events model. Biometrics 67, 1564-1572.
Anderson, J. R., Cain, K. C., and Gelber, R. D. (1983). Analysis of survival by tumor response. Journal of Clinical Oncology 1, 710-719. Austin, P. C. (2012). Generating survival times to simulate Cox proportional hazards models with time‐varying covariates. Statistics in Medicine 31, 3946-3958. Aydin, Z., Altunbasak, Y., and Borodovsky, M. (2006). Protein secondary structure prediction for a single-sequence using hidden semi-Markov models. BMC Bioinformatics 7, 178. Bartolomeo, N., Trerotoli, P., and Serio, G. (2011). Progression of liver cirrhosis to HCC: an application of hidden Markov model. BMC Medical Research Methodology 11, 38. Boberg, K. M., Rocca, G., Egeland, T., et al. (2002). Time-dependent Cox regression model is superior in prediction of prognosis in primary sclerosing cholangitis. Hepatology 35, 652-657. Bodger, K., Bowering, K., Sarkar, S., Thompson, E., and Pearson, M. G. (2011). All-cause mortality after first ERCP in England: clinically guided analysis of hospital episode statistics with linkage to registry of death. Gastrointestinal Endoscopy 74, 825-833. Chang, S. H. (2000). A two-sample comparison for multiple ordered event data. Biometrics 56, 183-189. Chang, S. H. (2004). Estimating marginal effects in accelerated failure time models for serial sojourn times among repeated events. Lifetime Data Analysis 10, 175-190. Chen, Y. H. (2012). Maximum likelihood analysis of semicompeting risks data with semiparametric regression models. Lifetime Data Analysis 18, 36-57. Choi, S., Lagakos, S. W., Schooley, R. T., and Volberding, P. A. (1993). CD4+ lymphocytes are an incomplete surrogate marker for clinical progression in persons with asymptomatic HIV infection taking zidovudine. Annals of Internal Medicine 118, 674-680. Clayton, D. G. (1978). A model for association in bivariate life tables and its application to epidemiological studies of familial tendency in chronic disease epidemilogy. Biometrika 65, 141-151. Cook, R. J., and Lawless, J. F. (1997). Marginal analysis of recurrent events and a terminating event. Statistics in Medicine 16, 911-924. Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society: Series B 34, 187-220. Day, R., Bryant, J., and Lefkopoulou, M. (1997). Adaptation of bivariate frailty models for prediction, with application to bilogical markers as prognostic indicatoers. Biometrika 84, 45-56. Ding, A. A., Shi, G., Wang, W., and Hsieh, J.-J. (2009). Marginal regression analysis for semi-competing risks data under dependent censoring. Scandinavian Journal of Statistics 36, 481-500. Fan, J., Hsu, L., and Prentice, R. L. (2000). Dependence estimation over a finite bivariate failure time region. Lifetime Data Analysis 6, 343-355. Fine, J. P., and Gray. R. J. (1999). A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association 94, 496-509. Fine, J. P., Jiang, H., and Chappell, R. (2001). On semi-competing risks data. Biometrika 88, 907-919. Fischer, M., Harvald, B., and Hauge, M. (1969). A Danish twin study of schizophrenia. The British Journal of Psychiatry 115, 981-990. Flandre, P., and O'Quigley, J. (1995). A two-stage procedure for survival studies with surrogate endpoints. Biometrics 51, 969-976. Fu, T.-C., Su, D.-H., and Chang, S.-H. (2015). Serial association analyses of recurrent gap time data via Kendall's tau. Biostatistics. 17, 188-202. Gail, M. H. (1972). Does Cardiac Transplantation Prolong Life? A reassessment. Annals of Internal Medicine 76, 815– 871. Ghosh, D. (2009). On assessing surrogacy in a single trial setting using a semicompeting risks paradigm. Biometrics 65, 521-529. Ghosh, D., and Lin, D. Y. (2000). Nonparametric analysis of recurrent events and death. Biometrics 56, 554-562. Ghosh, D., and Lin, D. Y. (2002). Marginal regression models for recurrent and terminal events. Statistica Sinica 12, 663-688. Ghosh, D., Taylor, J. M., and Sargent, D. J. (2012). Meta-analysis for surrogacy: accelerated failure time models and semicompeting risks modeling. Biometrics 68, 226-232. Hartmann, O., Schuetz, P., Albrich, W. C., Anker, S. D., Mueller, B., and Schmidt, T. (2012). Time-dependent Cox regression: serial measurement of the cardiovascular biomarker proadrenomedullin improves survival prediction in patients with lower respiratory tract infection. International Journal of Cardiology 161, 166-173. Hauge, M., Harvald, B., Fischer, M., et al. (1968). The Danish twin register. Acta Geneticae Medicae et Gemellogogiae. 17, 315-331. Hougaard, P. (2000). Analysis of Multivariate Survival Data. Springer-Verlag New York, Inc. Hsieh, J.-J., Wang, W., and Ding, A. A. (2008). Regression analysis based on semicompeting risks data. Journal of the Royal Statistical Society: Series B 70, 3-20. Huang, C. Y., and Wang, M. C. (2004). Joint modeling and estimation for recurrent event processes adn failure time data. Journal of the American Statistical Association 99, 1153-1165. Huang, X., and Liu, L. (2007). A joint frailty model for survival and gap times between recurrent events. Biometrics 63, 389-397. Kalbfleisch, J. D., and Prentice, R. L. (eds) (2002). The statistical analysis of failure time data, 2nd edition edition. Hoboken, New Jersey: Wiley. Kang, M., and Lagakos, S. W. (2007). Statistical methods for panel data from a semi-Markov process, with application to HPV. Biostatistics 8, 252-264. Kucukalic-Selimovic, E., Alagic, J., Valjevac, A., Hadzovic-Dzuvo, A., Begic, A., and Beslic, N. (2012). The value of serum thyreoglobulin levels and whole body (I-131) scintigraphy in the follow-up of the thyroid cancer patients after thyroidectomy. Collegium Antropologicum 36 Suppl 2, 67-71. Lagakos, S. W. (1976). A stochastic model for censored-survival data in the presence of an auxiliary variable. Biometrics 32, 551-559. Lagakos, S. W. (1977). Using auxiliary variables for improved estimates of survival time. Biometrics 33, 399-404. Lakhal, L., Rivest, L. P., and Abdous, B. (2008). Estimating survival and association in a semicompeting risks model. Biometrics 64, 180-188. Lancaster, A., and Intrator, O. (1998). Panel data with survival: Hospitalization of HIV-positive patients. Journal of the American Statistical Association 93, 46-53. Li, Q. H., and Lagakos, S. W. (1997). Use of the Wei-Lin-Weissfeld method for the analysis of a recurring and a terminating event. Statistics in Medicine 16, 925-940. Liao, C. S., Yang, K. C., Yen, M. F., and Chen, T. H. (2005). Time-varying predictors for clinical surveillance of small hepatocellular carcinoma. Cancer Journal 11, 226-233. Lim, H. J., and Zhang, X. (2011). Additive and multiplicative hazards modeling for recurrent event data analysis. BMC Medical Research Methodology 11, 101. Liu, C. Y., Wu, C. Y., Lin, J. T., Lee, Y. C., Yen, A. M., and Chen, T. H. (2006). Multistate and multifactorial progression of gastric cancer: results from community-based mass screening for gastric cancer. Journal of Medical Screening 13 Suppl 1, S2-5. Liu, L., Wolfe, R. A., and Huang, X. (2004). Shared frailty models for recurrent events and a terminal event. Biometrics 60, 747-756. Mauguen, A., Rachet, B., Mathoulin-Pelissier, S., Macgrogan, G., Laurent, A., and Rondeau, V. (2013). Dynamic prediction of risk of death using history of cancer recurrences in joint frailty models. Statistics in Medicine 32, 5366-5380. . Meira-Machado, L., de Una-Alvarez, J., Cadarso-Suarez, C., and Andersen, P. K. (2009). Multi-state models for the analysis of time-to-event data. Statistical Methods in Medical Research 18, 195-222. Nan, B., Lin, X., Lisabeth, L. D., and Harlow, S. D. (2006). Piecewise constant cross-ratio estimation for assoiciatio of age at a marker event ans age at menopause. Journal of the American Statistical Association 101, 65-77. Neri, A., Marrelli, D., Rossi, S., et al. (2007). Breast cancer local recurrence: risk factors and prognostic relevance of early time to recurrence. World Journal of Surgery 31, 36-45. Oakes, D. (1982a). A concordance test for independence in the presence of censoring. Biometrics 38, 451-455. Oakes, D. (1982b). A model for association in bivariate survival data. Journal of the Royal Statistitical Society, Series B 73, 414-422. Oakes, D. (1989). Bivariate survival models induced by frailties. Journal of the American Statistical Association 84, 487. Othus, M., and Li, Y. (2010). A Gaussian Copula Model for Multivariate Survival Data. Statistics in Biosciences 2, 154-179. Peng, L., and Fine, J. P. (2007). Regression modeling of semicompeting risks data. Biometrics 63, 96-108. Pintilie, M. (2007). Analysing and interpreting competing risk data. Statistics in Medicine 26, 1360-1367. Pollard, T. C., Villar, R. N., Norton, M. R., et al. (2010). Genetic influences in the aetiology of femoroacetabular impingement: a sibling study. The Journal of Bone and Joint Surgery. British volume 92, 209-216. Porta, N., Calle, M. L., Malats, N., and Gomez, G. (2012). A dynamic model for the risk of bladder cancer progression. Statistics in Medicine 31, 287-300. . Sargent, D. J., Wieand, H. S., Haller, D. G., et al. (2005). Disease-free survival versus overall survival as a primary end point for adjuvant colon cancer studies: individual patient data from 20,898 patients on 18 randomized trials. Journal of Clinical Oncology 23, 8664-8670. Schneider, L. S. (2013). Alzheimer disease pharmacologic treatment and treatment research. Continuum (Minneap Minn) 19, 339-357. Seifert, M., Gohr, A., Strickert, M., and Grosse, I. (2012). Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana. PLoS Computational Biology 8, e1002286. Shih, J. H., and Louis, T. A. (1995). Inferences on the association parameter in copula models for bivariate survival data. Biometrics 51, 1384-1399. Slutske, W. S., Cho, S. B., Piasecki, T. M., and Martin, N. G. (2013). Genetic overlap between personality and risk for disordered gambling: evidence from a national community-based Australian twin study. Journal of Abnormal Psychology 122, 250-255. Spiekerman, C. F., and Lin, D. Y. (1998). Marginal regression models for multivariate failure time data. The Journal of the American Statistical Association 93, 1164-1175. Su, D. H., Chang, S. H., and Chang, T. C. (2015). The impact of locoregional recurrences and distant metastases on the survival of patients with papillary thyroid carcinoma. Clinical Endocrinology 82, 286-294. Sweeting, M. J., Farewell, V. T., and De Angelis, D. (2010). Multi-state Markov models for disease progression in the presence of informative examination times: an application to hepatitis C. Statistics in Medicine 29, 1161-1174. Tan, Y. D., Fornage, M., George, V., and Xu, H. (2007). Parent-child pair design for detecting gene-environment interactions in complex diseases. Human Genetics 121, 745-757. Titman, A. C., and Sharples, L. D. (2010). Semi-Markov models with phase-type sojourn distributions. Biometrics 66, 742-752. Vefring, H. K., Wee, L., Jugessur, A., Gjessing, H. K., Nilsen, S. T., and Lie, R. T. (2010). Maternal angiotensinogen (AGT) haplotypes, fetal renin (REN) haplotypes and risk of preeclampsia; estimation of gene-gene interaction from family-triad data. BMC Medical Genetics 11, 90. Verburg, F. A., Mader, U., Tanase, K., et al. (2013). Life expectancy is reduced in differentiated thyroid cancer patients >/= 45 years old with extensive local tumor invasion, lateral lymph node, or distant metastases at diagnosis and normal in all other DTC patients. The Journal of Clinical Endocrinology and Metabolism 98, 172-180. Wang, M. C., and Chang, S. H. (1999). Nonparametric estimation of a recurrenct survival function. Journal of the American Statistical Associaton 94, 146-153. Wang, W. (2003). Estimating the association parameter for copula models under dependent censoring. Journal of the Royal Statistical Society: Series B 65, 257-273. Wei, L. J., Lin, D. Y., and Weissfeld, L. (1989). Regression analysis of multivariate incomplete failure time data by modelling marginal distributions. Journal of the American Statistical Association 84, 1065-1073. Whitmore, G. A., Crowder, M. J., and Lawless, J. F. (1998). Failure inference from a marker process based on a bivariate Wiener model. Lifetime Data Analysis 4, 229-251. Williams, M. E., Lacson, E., Jr., Wang, W., Lazarus, J. M., and Hakim, R. (2010). Glycemic control and extended hemodialysis survival in patients with diabetes mellitus: comparative results of traditional and time-dependent Cox model analyses. Clinical Journal of the American Society of Nephrology 5, 1595-1601. Wu, H. M., Yen, M. F., and Chen, T. H. (2004). SAS macro program for non-homogeneous Markov process in modeling multi-state disease progression. Computer Methods and Programs in Biomedicine 75, 95-105. Xu, R., and O'Quigley, J. (2000). Proportional hazards estimate of the conditional survival function. Journal of the Royal Statistitical Society, Series B 62, 667-680. Ye, Y., Kalbfleisch, J. D., and Schaubel, D. E. (2007). Semiparametric analysis of correlated recurrent and terminal events. Biometrics 63, 78-87. Zhang, W., Fan, J., and Sun, Y. (2009). A semiparametric model for cluster data. Annals of Statistics 37, 2377-2408. Zhang, X., Zhang, M. J., and Fine, J. (2011). A proportional hazards regression model for the subdistribution with right-censored and left-truncated competing risks data. Statistics in Medicine 30, 1933-1951. Zhao, X., Liu, L., Liu, Y., and Xu, W. (2012). Analysis of multivariate recurrent event data with time-dependent covariates and informative censoring. Biometrical Journal 54, 585-599. Zoppini, G., Targher, G., Chonchol, M., et al. (2012). Predictors of estimated GFR decline in patients with type 2 diabetes and preserved kidney function. Clinical Journal of the American Society of Nephrology 7, 401-408. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3972 | - |
dc.description.abstract | 以臨床工作而言,診療慢性患者的過程所形成的記錄,為一長期追踨資料型態。在每次患者就診中,臨床醫師會透過一些標誌或事件訊息來了解未來患者的併發症或死亡風險的高低,根據這些風險的高低,醫師需要制定適當的措施來防制或延緩併發症甚至死亡的發生,故如何量化此類風險是臨床上很重要的問題。本論文的目的在於利用患者的動態指標訊息及基本特性來預測未來的存活機率,亦即利用標誌的訊息,不斷地更新對未來存活的預測。時間依賴性Cox模式(time-dependent Cox’s model)是分析存活資料的一種常用的迴歸模式,具有利用長期追蹤資料具時間順序的特色,而明確地架構標誌歷程和基礎共變數跟終止事件間的相關性。但採用以標誌歷程為時間依賴性共變數的Cox模式的問題在於此時間依賴性模式為一即時解釋模式,無法用過去標誌歷程直接預測未來存活機率。因此本論文運用貝氏定理作機率的反轉及條件式機率的運算來處理這個問題,如此便可利用共變數和標誌歷程的條件式分布,在發生終止事件的時間點上給予適當的權重上(Cox模式中的參數及變數值所構成),而可在不同標誌歷程及共變數的條件下,估計未來存活機率。此方法之優點在於可免於估計時間依賴性Cox模式中的基礎風險函數及標誌時間的邊際分布。本研究將利用模擬以測試所提出方法之表現,並以甲狀腺乳突癌患者為例加以說明所提之方法的運用。 | zh_TW |
dc.description.abstract | In clinical practice, the records of patients with chronic diseases is a form of the longitudinal data. At each patient’s visit, the physician will collect the signs or event information to understand the level of the patient's future risk of complications or death. According to the level of these risks, physicians need to take some appropriate actions to prevent or delay the occurrence of complications or death. So, how to quantify such risks is a clinically important issue. The purpose of this paper is to use the dynamic messages of marker and the patients’ basic characteristics to predict the patients’ survival. Time-dependent Cox’s model is a population regression model which constructs explicit dependence of the hazard of termination time on baseline covariates and marker process by taking the advantage of longitudinal data with chronological features. However, in the time-dependent Cox’s model, the effect of the marker on the immediate survival has no meaning of prediction. That is, it is not straightforward to predict the future survival given the past information of the marker process in the time-dependent Cox’s model. Therefore, we adopt Bayes' theorem and conditional probability to overcome such problems. We estimate the conditional probability of future survival given the different information of marker process by using the conditional distribution of baseline covariates and marker process given surviving at a time point and the Cox modeling information. The advantage of the proposed method is that marginal distribution of marker process and baseline hazard function in the Cox’s model are not required. Simulation studies are conducted to assess the performance of the proposed method. An example of papillary thyroid carcinoma is provided for illustration. | en |
dc.description.provenance | Made available in DSpace on 2021-05-13T08:39:30Z (GMT). No. of bitstreams: 1 ntu-105-D97842007-1.pdf: 2169659 bytes, checksum: 70331dadf379fab421699eb38b392132 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 摘要 iii
第一章 背景和目的 1 第二章 文獻回顧 5 1.多變數存活資料的分類及特性 5 (Ⅰ) 平行資料 5 (Ⅱ) 長期資料 7 2.多變數存活資料的統計分析 11 (Ⅰ) 相依性分析 12 (Ⅱ) 邊際模型的分析 15 (Ⅲ) 條件與聯合模型 16 (Ⅳ) 多階段模式 17 3.標誌歷程 18 (Ⅰ) 標誌歷程的應用 19 (Ⅱ) 右受限的標誌歷程 20 (Ⅲ) 前進式三階段資料 20 (Ⅳ) 前進式三階段資料之統計分析 21 4.不同Cox模式對時間依賴性變數之處理 24 5.所用之甲狀腺乳突癌資料 24 第三章.方法 27 1.符號及資料結構 27 2.模型 32 3.估計 34 (Ⅰ) 以右受限資料估計以標誌歷程為條件的未來存活函數 34 (Ⅱ) 以存活者的後續有限觀察時間條件存活機率之預測 46 4.估計量的統計性質 47 第四章 模擬及結果 52 1.資料生成 52 2.模擬結果 53 第五章 實例說明 57 第六章 討論和結論 62 參考文獻 90 附錄 104 附錄A 甲狀腺癌預後因子研究之台大醫院研究倫理委員會通過函 104 附錄B 無共變數下,條件存活機率 及 的推導 107 附錄 C 條件存活機率 及 的推導 116 附錄D 輔助定理的証明 123 | |
dc.language.iso | zh-TW | |
dc.title | 標誌歷程之動態存活預測的統計分析 | zh_TW |
dc.title | Statistical Analysis for Dynamic Survival Prediction Involving Marker Processes | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 張天鈞,戴政,陳秀熙,丘政民,鄭宗記 | |
dc.subject.keyword | 追蹤資料,標誌歷程,雙變數存活,多變數存活,存活預測, | zh_TW |
dc.subject.keyword | longitudinal data,marker process,bivariate survival,multivariate survival,prediction, | en |
dc.relation.page | 125 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2016-02-15 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
顯示於系所單位: | 流行病學與預防醫學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-105-1.pdf | 2.12 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。