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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 洪弘(Hung Hung) | |
dc.contributor.author | Yueh Wang | en |
dc.contributor.author | 王悅 | zh_TW |
dc.date.accessioned | 2021-06-17T09:09:12Z | - |
dc.date.available | 2022-03-12 | |
dc.date.copyright | 2020-03-12 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-10-17 | |
dc.identifier.citation | Andersson, T., Dickman, P., Eloranta, S., & Lambert, P. (2011). Estimating and modelling cure
in populationbased cancer studies within the framework of flexible parametric survival models. BMC Medical Research Methodology, 11(96). Baade, P., Youlden, D., & Chambers, S. (2011). When do I know I am cured? Using conditional estimates to provide better information about cancer survival prospects. The Medical Journal of Australia, 194(2), 73-77. Blakely, T., Costilla, R., & Tobias, M. (2010). The burden of cancer: New Zealand 2006. Wellington: New Zealand Ministry of Health. Blakely, T., Foster, R., Wilson, N., & Team, B. (2012). Burden of disease epidemiology, equity and cost-effectiveness (BODE3) study protocol. Version 2.1. Wellington: Department of Public Health, University of Otago, Wellington, Technical Report(3). Castillo, J., Winer, E., & Olzewski, A. (2013). Population-based prognostic factors for survival in patients with Burkitt lymphoma. Cancer, 119(20), 3672-3679. Dal Maso, L., Guzzinati, S., Buzzoni, C., Capocaccia, R., Serraino, D., Caldarella, A., … the AIRTUM Working group (2014). Long-term survival, prevelance, and cure of cancer: a population-based estimation for 818,902 Italian patients and 26 cancer types. Annals of Oncology, 25, 2251-2260. De Angelis, R., Capocaccia, R., Hakulinen, T., Söderman, B., & Verdecchia, A. (1999). Mixture models for cancer survival analysis: application to population-based data with covariates. Statistics in Medicine, 18, 441-454. Dubecz, A., Gall, I., Solymosi, N., Schweigert, M., Peters, J., Feith, M., & Stein, H. (2012). Temporal trends in long-term survival and cure rates in esophageal cance - a SEER database analysis. Journal of Thoracic Oncology, 7(2), 443-447. Ederer, F., Axtell, L., & Cutler, S. (1961). The relative survival rate: a statistical methodology. National Cancer Institute monographs, 6, 101-121. GBD. (2016). Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: A systematic analysis for the global burden of disease study 2015. Lancet, 388, 1545-1602. Hakulinen, T. (1982). Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics, 38, 933-942. Howlader, N., Ries, L., Mariotto, A., Reichman, M., Ruhl, J., & Cronin, K. (2010). Improved estimates of cause-specific survival rates from population-based data. Journal of the National Cancer Institute, 102, 1584-1598. Huang, B., Guo, J., & Charnigo, R. (2014). Statistical methods for population-based cancer survival in registry data. Journal of Biometrics and Biostatistics, 5. Janssen-Heijnen, M., Condos, A., Bray, F., T., H., D.H., B., Brenner, H., & Coebergh, J. (2010). Clinical relevance of conditional survival of cancer patients in Europe: age-specific analyses of 13 cancers. Journal of Clinical Oncology, 28(15), 2520-2528. Janssen-Heijnen, M., Houterman, S., Lemmens, V., Brenner, H., Steyerberg, E., & Coebergh, J. (2007). Prognosis for long-term survivors of cancer. Annals of Oncology, 18(8), 1408-1413. Lambert, P., Thompson, J., Weston, C., & Dickman, P. (2007). Estimating and modeling the cure fraction in population-based cancer survival analysis. Biostatistics, 8(3), 576-594. Luenberger, D., & Ye, Y. (2008). Linear and nonlinear programming (4th ed.). Ma, S., & Huang, J. (2007). Combining multiple markers for classification using ROC. Biometrics, 63, 751-757. Naghavi, M., Makela, S., Foreman, K., O’Brien, J., Pourmalek, F., & Lozano, R. (2010). Algorithms for enhancing public health utility of national causes-of-death data. Population Health Metrics, 8(9). Nieto-Barajas, L., & Yin, G. (2008). Bayesian semeparametric cure rate model with an unknown threshold. Scandinavian Journal of Statistics, 35(3), 540-556. Pohar-Perme, M., Stare, J., & Estève, J. (2012). On estimation in relative survival. Biometrics, 68, 113-120. Smoll, N., Schaller, K., & Gautschi, O. (2012). The cure fraction of glioblastoma multiforme. Neuroepidemiology, 39, 63-69. Woods, L., Rachet, B., Lambert, B., & Coleman, M. (2009). ‘Cure’ from breast cancer among two populations of women followed for 23 years after diagnosis. Annals of Oncology, 20(8), 1331-1336. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74869 | - |
dc.description.abstract | 在族群癌症存活分析研究當中,淨存活率是最常被用到來衡量國家健康的指標之一。近幾十年當中,研究者常觀察到淨存活率在長時間追蹤後呈現出平緩的趨勢,一般將此看似緩解之族群現象稱為「統計治癒」。近年來已有發展完善之統計方法用以探討統計治癒之現象。除此之外,達到統計治癒之時間可被用於政府對於重大傷病證明續發之參考依據、以及在臨床具有便於病患理解預後之便利性,治癒時間不論在政府制定決策或是臨床上皆能提供幫助。然而,傳統探討統計治癒的方法皆假設治癒時間為無限大,這對於後續的治癒時間推論造成了一定程度的困難。在此博士論文中,我們從條件存活率出發,定義出更加廣義的統計治癒概念。在新定義之統計治癒中,治癒時間是被允許存在的,我們也將治癒時間的統計理論基礎展示於此論文中。我們進一步建立了治癒時間模型之統計方法來估計治癒時間,並且透過模擬展示了治癒時間的統計性質。在資料分析中,我們使用台灣癌症登記資料庫為例,計算了 22 個主要癌症之治癒時間,並且進一步以結直腸癌為例,納入性別、年齡分層、及期別建立治癒時間模型以進行假設檢定等統計推論。此博士論文主要的貢獻為定義更廣義之統計治癒概念,並且提供一個具統計理論基礎的治癒時間估計方法。我們期望透過此法估計而得的治癒時間在未來能對公共衛生政策制訂提供更精準的參考依據。 | zh_TW |
dc.description.abstract | In population-based cancer survival analysis, the net survival is important for government to assess health care programs. For decades, it is observed that the net survival reaches a plateau after long-term follow-up, this is so called “statistical cure”. Several methods were proposed to address the statistical cure. Besides, the cure time can be used to evaluate the time period of a health care program for a specific patient population, and it also can be helpful for a clinician to explain the prognosis for patients, therefore the cure time is an important health care index. However, those proposed methods assume the cure time to be infinity, thus it is inconvenient to make inference on the cure time. In this dissertation, we define a more general concept of statistical cure via conditional survival. Based on the newly defined statistical cure, the cure time is well defined. We develop cure time model methodologies and show a variety of properties through simulation. In data analysis, cure times are estimated for 22 major cancers in Taiwan, we further use colorectal cancer data as an example to conduct statistical inference via cure time model with covariate sex, age group, and stage. This dissertation provides a methodology to obtain cure time estimate, which can contribute to public health policy making. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T09:09:12Z (GMT). No. of bitstreams: 1 ntu-108-D02849004-1.pdf: 1714327 bytes, checksum: 12c89a5d46aa4664ed76ed8892a76e8d (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 摘要i
Abstract ii 1 Introduction 1 1.1 Population-based cancer survival analysis . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Statistical cure and cure rate model . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Cure time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 A New Perspective of Statistical Cure with Cure time 8 3 Statistical Inference Procedure of Cure Time 13 3.1 Data structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Model specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.3 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4.1 Estimation of α given β . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.4.2 Estimation of β given α . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.4.3 Estimation of (α, β)T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.5 Standard error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4 Simulation Studies 27 4.1 Simulation results under (S1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.2 Simulation results under (S2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.3 Simulation results under (S3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5 Data Analysis 35 5.1 Cure time estimation of 22 major cancers in Taiwan . . . . . . . . . . . . . . . . 35 5.2 Taiwan colorectal cancer data analysis . . . . . . . . . . . . . . . . . . . . . . . 40 6 Discussion 45 References 48 A Proof of Lemma 1 51 B Proof of Lemma 2 52 C Proof of Theorem 1 53 D Proof of Theorem 2 54 E Figures of ST (t|k) and SO(t|k) of 21 major cancers in Taiwan 56 | |
dc.language.iso | en | |
dc.title | 治癒時間之統計推論 | zh_TW |
dc.title | Statistical inference on the cure time | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-1 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 李文宗(Wen-Chung Lee),杜裕康(Yu-Kang Tu),林莞俞(Wan-Yu Lin),蕭朱杏(Chuhsing Kate Hsiao) | |
dc.subject.keyword | 淨存活率,條件存活率,統計治癒,治癒率,治癒時間, | zh_TW |
dc.subject.keyword | net survival,conditional survival,statistical cure,cure rate,cure time, | en |
dc.relation.page | 77 | |
dc.identifier.doi | 10.6342/NTU201904221 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-10-18 | |
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 |
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