簡易檢索 / 詳目顯示

研究生: 魏立渝
Wei, Li-Yu
論文名稱: 區間設限資料在加速失效模型下之結果依賴採樣設計
Accelerated failure time modeling for interval-censored failure time data under outcome-dependent sampling design
指導教授: 呂翠珊
Lu, Tsui-Shan
口試委員: 張少同
Chang, Shao-Tung
徐雅甄
Hsu, Ya-Chen
呂翠珊
Lu, Tsui-Shan
口試日期: 2022/06/29
學位類別: 碩士
Master
系所名稱: 數學系
Department of Mathematics
論文出版年: 2022
畢業學年度: 110
語文別: 英文
論文頁數: 30
中文關鍵詞: 偏差抽樣結果依賴採樣區間設限資料加速失效模型
英文關鍵詞: Biased sampling, ODS design, Interval-censored data, Accelerated failure time model
DOI URL: http://doi.org/10.6345/NTNU202201056
論文種類: 學術論文
相關次數: 點閱:168下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 區間設限資料經常出現在當存活時間無法直接地被觀察到的縱貫型研究或臨床試驗中,我們唯一能得知的只有時間是落在哪個特定區間內。事實上,區間設限資料在近年來越來越常出現於大型的世代研究中,但世代研究所需的花費在有限的經費預算下,已經變成研究者們沉重的負擔。研究人員希望能找出一個比簡單隨機抽樣更具成本效益的抽樣方法來減少研究的支出。近年來,結果依賴採樣(ODS) 已被視為一個具成本效益的抽樣設計並應用在許多流行病學或大型資料庫的研究中,此設計的核心價值是希望能選取更具資訊量的樣本。在本次研究中,我們發展出針對區間設限資料在加速失效(AFT) 模型下兩種不同的結果依賴採樣設計並在相同樣本數下比較簡單隨機抽樣(SRS) 以及結果依賴採樣(ODS) 的估計表現。從模擬結果顯示,在不同配置下的相同樣本數進行抽樣,結果依賴採樣比簡單隨機抽樣的估計結果表現更佳。最後,我們也將此設計應用於Signal Tandmobiel 研究中。

    Interval-censored data arise in survival analysis when the failure time can not be observed directly, and the only thing we know is the particular interval where the time is located. As the cost of a large cohort study has become an unbearable burden for researchers because of the limited budget, researchers want to search for a more cost-effective design other than just simple random sampling to lower the cost of the study. In recent years, an outcome-dependent sampling (ODS) design is regarded as a cost-effective sampling scheme and has been widely applied in many biomedical and epidemiological studies. The core value of this design is to include more informative failure subjects from the supplemental components particularly interested. In our study, we develop two ODS designs for the interval-censored failure time data under the accelerated failure time (AFT) model and compare the performance of the estimates from simple random sampling (SRS) and ODS designs. Under different settings in the simulation studies, the results show that the estimator from the ODS design is more efficient than that under the SRS design of the same sample size. We then apply the proposed designs to the Signal Tandmobiel study.

    Chapter 1 Introduction 1 Chapter 2 Interval-Censoring ODS Design and Statistical Inference Method 5 2.1 Interval-Censored data 5 2.2 Interval-Censoring ODS design 6 2.3 Statistical inference method 8 Chapter 3 Simulation Study 11 3.1 Data generation 11 3.2 Results 13 Chapter 4 Analysis of the Signal Tandmobiel Study 23 Chapter 5 Conclusions and Discussions 26 References 28

    [1] J. A. Wellner. (1995). Interval censoring case 2: alternative hypotheses. in analysis
    of censored data, proceedings of the workshop on analysis of censored data. IMS
    Lecture Notes, Monograph Series, 27:271–291.
    [2] J. Huang and J. A. Wellner. (1997). Interval censored survival data: a review
    of recent progress. In Proceedings of the first seattle symposium in biostatistics,
    pages 123–169.
    [3] A. Schick and Q. Yu. (2000). Consistency of the gmle with mixed case intervalcensored
    data. Scandinavian Journal of Statistics, 27:45–55.
    [4] J. Vanobbergen, L. Martens, E. Lesaffre, and D. Declerck. (2000). The signaltandmobiel
    project a longitudinal intervention health promotion study in flanders
    (belgium): baseline and first year results. European Journal of Paediatric Dentistry,
    2:87–96.
    [5] J. Cornfield. (1951). A method of estimating comparative rates from clinical data.
    applications to cancer of the lung, breast, and cervix. Journal of the National
    Cancer Institute, 11:1269–1275.
    [6] R. L. Prentice. (1986). A case-cohort design for epidemiologic studies and disease
    prevention trials. Biometrika, 73:1–11, 1986.
    [7] H. Zhou, M. A. Weaver, J. Qin, M. P. Longnecker, and M. C. Wang. (2002). A
    semiparametric empirical likelihood method for data from an outcome-dependent
    sampling scheme with a continuous outcome. Biometrics, 58:413–421.
    [8] N. Chatterjee, Y. H. Chen, and N. E. Breslow. (2003). A pseudoscore estimator
    for regression problems with two-phase sampling. Journal of the National Cancer
    Institute, 98:158–168.
    [9] M. A. Weaver and H. Zhou. (2005). An estimated likelihood method for continuous
    outcome regression models with outcome-dependent sampling. Journal of the
    American Statistical Association, 100:459–469.
    [10] H. Zhou, J. Chen, T. H. Rissanen, S. A. Korrick, H. Hu, J. T. Salonen, and M.
    P. Longnecker. (2007). Outcome-dependent sampling: an efficient sampling and
    inference procedure for studies with a continuous outcome. Epidemiology, 18:461–
    468.
    [11] R. Song, H. Zhou, and M. R. Kosorok. (2009). A note on semiparametric efficient
    inference for two-stage outcome-dependent sampling with a continuous outcome.
    Biometrika, 96:221–228.
    [12] H. Zhou, R. Song, Y. Wu, and J. Qin. (2011). Statistical inference for a twostage
    outcome-dependent sampling design with a continuous outcome. Biometrics,
    67:194–202.
    [13] J. Ding, H. Zhou, Y. Liu, J. Cai, and M. P. Longnecker. (2014). Estimating effect
    of environmental contaminants on women's subfecundity for the moba study data
    with an outcome-dependent sampling scheme. Biostatistics, 15(4):636–650.
    [14] J. Yu, Y. Liu, D. P. Sandler, and H. Zhou. (2015). Statistical inference for the
    additive hazards model under outcome-dependent sampling. Canadian Journal of
    Statistics, 43:436–453.
    [15] Q. Zhou, J. Cai, and H. Zhou. (2018). Outcome-dependent sampling with intervalcensored
    failure time data. Biometrics, 74(1):58–67.
    [16] L. J. Wei. (1992). The accelerated failure time model: a useful alternative to the
    cox regression model in survival analysis. Statistics in medicine, 11(14-15):1871–
    1879.
    [17] J. D. Kalbfleisch and R. L. Prentice. (2011). The statistical analysis of failure time
    data. John Wiley & Sons.
    [18] K. Kiani and J. Arasan. (2012). Simulation of interval censored data in medical and
    biological studies. International Journal of Morden Physics: Conference Series,
    09:112–118.
    [19] Z. Zhang and J. Sun. (2010). Interval censoring. Statistical methods in medical
    research, 19(1):53–70.

    無法下載圖示 電子全文延後公開
    2027/08/10
    QR CODE