存活與長期追蹤資料之聯合模型－台灣愛滋病實例研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：56

、訪客IP：3.133.116.189

姓名

施伊珊(Yi-shan Shih) 查詢紙本館藏

畢業系所

統計研究所

論文名稱

存活與長期追蹤資料之聯合模型－台灣愛滋病實例研究
(Joint Model for Survival and Longitudinal Data － A Case Study on Taiwan AIDS Data)

相關論文

★ 長期與存活資料之聯合模型-新方法和數值方法的改進	★ 復發事件存活分析的共享廣義伽瑪脆弱因子之半母數聯合模型
★ 加乘法風險模型結合長期追蹤資料之聯合模型	★ 有序雙重事件時間分析使用與時間相關的共變數－邊際方法的比較
★ 以聯合模型探討地中海果蠅繁殖力與老化之關係	★ 聯合模型在雞尾酒療法療效評估之應用—利用CD4/CD8比值探討台灣愛滋病資料
★ 時間相依共變數之雙重存活時間分析—台灣愛滋病病患存活時間與 CD4 / CD8 比值關係之案例研究	★ Cox比例風險模型之參數估計─比較部分概似法與聯合模型
★ 復發事件存活時間分析-丙型干擾素對慢性肉芽病患復發療效之案例研究	★ Cox 比例風險假設之探討與擴充風險模型之應用
★ 以聯合模型探討原發性膽汁性肝硬化	★ 聯合長期追蹤與存活資料分析－肝硬化病患之實例研究
★ 復發事件存活時間分析-rhDNase對囊狀纖維化病患復發療效之案例研究	★ 聯合長期追蹤與存活資料分析-原發性膽汁性肝硬化病患之實例研究
★ 復發事件存活時間分析-Thiotepa對膀胱癌病患復發療效之案例研究	★ 半母數擴充風險模型

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

十多年來，關於高效抗逆轉錄病毒治療的研究已有許多，也由諸多的數據證實使用此種療法確實可以延長發病時間與降低死亡率。本篇使用141個已發病的台灣愛滋病人資料，分為有無使用雞尾酒療法兩組，研究的目的為探討雞尾酒療法對於已發作的愛滋病人的存活時間的影響及其CD4值的變化情況。我們主要使用兩個方法：圖形法，用以初步瞭解資料所包含的訊息，得到CD4值和使用雞尾酒療法與存活機率為正相關的可能結論。聯合模型，因為長期性資料的測量時間不固定而造成測量誤差，但在重複測量的次數夠多且誤差較小的情況下，使用聯合模型所得到的估計值具有一致性、近似常態與有效性等好的性質。我們得到的結果與之前研究的結果都是一致的，使用雞尾酒療法可以使CD4值上升而達到延長生命的結果。

摘要(英)

Over the past decade, much research have been done on Highly Active Antiretroviral Therapy (HAART), which has been proven to be able to postpone morbidity and lower mortality rate. This research collects data from 141 AIDS patients in Taiwan, which are separated into two groups, HAART and non-HAART. The research objective is to investigate the effect HAART and the CD4 cells to lifetime of AIDS patients. Research methods include graphic techniques and joint-modeling approach. Graphic techniques allow us to understand basic information from data, and the relationship between CD4 cells and mortality rate with the use of HARRT. Biased estimation usually occurs in the longitudinal data with intermittent measurement schedule. Joint modeling approach can correct this bias. Furthermore, the estimates derived by joint model have been proved to be consistent and efficient. Our findings are consistent with previous studies. The use of HARRT enables the increase of CD4 cells and extension of patients’ lifetime.

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 v
表目錄 vi
第一章緒論 1
第二章台灣愛滋病人資料與背景 6
第三章統計方法 8
3.1 圖形法（graphic method） 8
3.1.1 事件歷史圖 8
3.1.2 3-D平滑曲面圖 9
3.1.3 CD4值的量變曲線圖 10
3.2 聯合模型（joint model） 10
第四章實例分析 16
第五章結論與討論 24
參考文獻 27

參考文獻

1.Becker RA, Chambers JM and Wilks AR. The New S Language: a programming environment for data analysis and graphics. Chapman & Hall, June 1988.
2.Dubin J, Muller HG, Wang JL. Event history graphs of censored survival data. Statistics in Medicine 2001；20:2951-2964.
3.Egger M, Hirschel B, Francioli P et al. Impact of new antiretroviral combination therapies in HIV infected patients in Switzerland: prospective multicentre study. Swiss HIV Cohort Study. BMJ 1997; 315(7117):1194-9
4.Fang CT, Hsu HM, Twu SJ, et al. Decreased HIV transmission after a policy of providing free access to highly active antiretroviral therapy in Taiwan. J Infect Dis. 2004; 190(5):879-85.
5.Gulick RM, Meibohm A, Havlir D, et al. Six-year follow-up of HIV-1-infected adults in a clinical trial of antiretroviral therapy with indinavir, zidovudine and lamivudine. AIDS 2003; 17:2345-9.
6.Henderson , R., Diggle, P. & Dobson, A. (2000). Joint modelling of longitudinal measurements and event time data. Biostatistics 4, 465–480.
7.Hsieh F, Tseng YK, Wang JL. Joint Modeling of Survival Time and Longitudinal Data: Likelihood Approach Revisit. Biometrics, 62, 1037-1043, 2006.
8.Kaufmann GR, Perrin L, Pantaleo G, et al. CD4 T-lymphocyte recovery in individuals with advanced HIV-1 infection receiving potent antiretroviral therapy for 4 years: the Swiss HIV Cohort Study. Arch Intern Med. 2003; 163:2187-95.
9.Lucas CM, Chaisson RE, Moore RD. Highly active antiretroviral therapy in a large urban clinic: risk factors for virologic failure and adverse drug reactions. Ann Intern Med 1999; 131:81-87.
10.Prentice RL. Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika, 69, 331-342, 1982.
11.Pawitan Y and Self S. Modeling disease marker process in AIDS. Journal of the American Statistical Association, Vol. 88, No. 423. (Sep., 1993), 719-726
12.Palella FJ, Delaney KM, Moorman AC, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med. 1998; 338(13):853-60.
13.Tsiatis AA, DeGruttola V & Wulfsohn MS (1995). Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association 90, 27-37.
14.Tsiatis AA & Davidian M (2001). A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error. Biometrika 88, 447–458.
15.Tsiatis AA and Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica 2004; 14:809-34.
16.Tseng YK, Hsieh F and Wang JL. Joint modeling of accelerated failure time and longitudinal data. Biometrika 2005; 92:587-603.
17.Wulfsohn MS, Tsiatis AA. A Joint Model for Survival and Longitudinal Data Measured with Error. Biometrics, Vol. 53, No. 1. (Mar., 1997), 330-339.
18.Wang Y & Taylor JMG (2001). Jointly modeling longitudinal and event time data with application to acquired immunodeficiency syndrome. J. Am. Statist. Assoc. 96, 895–905.
19.Yu M, Law NJ, Taylor JMG & Sandler HM (2004). Joint longitudinal-survival-cure models and their application to prostate cancer. Statist. Sinica 14, 835–62.
20.Zeng D, Cai J. Asymptotic results for maximum likelihood estimators in joint analysis of repeated measurements and survival time. The Annals of Statistics 2005; 33(5):2132-63.

指導教授

曾議寬(Yi-kuan Tseng)

審核日期

2007-6-26

推文