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系統識別號 U0002-1907201811241200
中文論文名稱 比例勝算模型下現狀數據的樣本數計算
英文論文名稱 Sample Size Calculation for the Proportional Odds Model with Current Status Data
校院名稱 淡江大學
系所名稱(中) 數學學系數學與數據科學碩士班
系所名稱(英) Master's Program, Department of Mathematics
學年度 106
學期 2
出版年 107
研究生中文姓名 楊芳甄
研究生英文姓名 Fang-Chen Yang
學號 604190057
學位類別 碩士
語文別 中文
口試日期 2018-06-26
論文頁數 31頁
口試委員 指導教授-温啓仲
委員-黃逸輝
委員-吳裕振
中文關鍵字 存活分析  檢定力  研究設計 
英文關鍵字 Survival Analysis  Power  Study Design 
學科別分類
中文摘要 在現狀設限數據的研究中,感興趣的毀壞事件是一次檢查設限的,所得的資訊僅為檢查時間,和在此時間毀壞事件是否發生的狀態指標。對於現狀數據分析,Willamson, Lin and Kim (2009)在基線分布為Weibull的比例風險模型下,提供了一個樣本數計算方法,然而一個現狀數據不必然服從比例風險模型和Weibull基線分布假設。
本論文考慮包含Weibull, Log-Logistic, Log-Normal等等,較具彈性的基線分布(Sparling et al, 2006),在比例勝算模型,提出一個現狀數據的樣本數公式。模擬試驗驗證了公式的正確性,使用了兩個真實研究案例,作為樣本數公式的應用例證。
英文摘要 In the study of current status data, when the failure of interest examined once, the available information are the examination time and the indicator of the failure has occurred or not by the examination time. Willamson, Lin and Kim (2009) proposed a sample size formula for current status data under the proportional hazards model with Weibull baseline distribution. However, a current status sample is not necessary to follow the proportional hazards model or satisfy the Weibull baseline assumption.
In this thesis we consider another popular survival model, the proportional odds model, with a flexible baseline distribution (Sparling et al. 2006) includes Weibull, Log-Logistic. Log-Normal distributions. Under this model, we proposed a sample size formula for current status data. Simulations certify the validity of the formula, two real examples illustrate the applications of the method.
論文目次 目錄
1. 前言...................................................第1頁
2. 方法...................................................第3頁
3. 模擬試驗........................................第10頁
4. 實例分析........................................第20頁
4-1 老鼠肺腫瘤實驗..................第20頁
4-2 老鼠肺腫瘤實驗..................第23頁
5. 結論與討論....................................第26頁
6. 參考文獻........................................第27頁
7. 附錄.................................................第29頁
參考文獻 1. Cantor, A. B. (1992). ”Sample size calculations for the log rank test: A Gompertz model
approach,” J Clin Epidemiol, 45(10): 1131-1136.
2. Eng, K. H. and Kosorok, M. R. (2005). ”A sample size formula for the supremum log-rank
statistic,” Biometrics, 61: 86-91.
3. Gail, M. H. (1985). ”Applicability of sample size calculations based on a comparison of
proportions for use with the log rank test,” Controlled Clinical Trials, 6: 112-119.
4. Hsieh, F. Y. and Lavori, P. W. (2000). ”Sample size calculations for the Cox proportional
hazards model with nonbinary covariates,” Controlled Clinical Trials, 21(6): 552-560.
5. Hoel, D. G. and Walburg, H. E. (1972). ”Statistical analysis of survival experiments,”
Journal of National Cancer Institute, 49: 361-372.
6. Huang, J. and Wellner, A. J. (1996). ”Interval censored survival data: a review of recent
progress,” In Proceeding of the First Seattle Symposium in Biostatistics, Springer-Verlag,
New York, 123-169.
7. Jung, S. H. (2008). ”Sample size calculation for the weighted rank statistics with paired
survival data,” Statistics and Medicine, 27: 3350-3365.
8. Ma, S. (2009). ”Cure model with current status data,” Statistica Sinica, 19: 233-249.
9. Martinussen, T. and Scheike, T. H. (2002). ”Efficient estimation in additive hazards regression
with current status data,” Biomertrika, 89: 649-58.
10. Rossini, A. J. and Tsiatis, A. A. (1996). ”A semiparametric proportional odds regression
model for the analysis of current status data,” JASA, 91: 713-721.
11. Ryan, T. P. (2013). Sample Size Determination and Power. Hoboken, New Jersey: John
Wiley & Sons.
12. Schoenfeld, D. (1983). ”Sample-size formula for the proportional- hazards regression
model,” Biometrics, 39: 499-503.
13. Sparling, Y. H. , Younes, N. , Lachi, J. M. and Bautista, O. M.(2006). ”Parametric survival
models for interval-censored data with time-dependent covariates,” Biostatistics, 7: 599–
614.
14. Sun, J. and Sun, L. (2005). ”Semiparametric linear transformation models for current
status data,” The Canadian Journal of Statistics, 33: 85-96.
15. Tian, L. and Cau, T. (2006). ”On the accelerated failure time model for current status and
interval censored data,” Biometrika, 93: 329-342.
16. Williamson, J. M. , Lin H. M. and Kim, H. Y. (2009). ”Power and sample size calculations
for current status survival analysis,” Statistics in Medicine, 28: 1999-2011.
17. Cheng, W. L. (2017). ”Sample Size Calculation for the Proportional Hazards Model with
Current Status Data,” master thesis, Tamkang University.
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