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姓名 許根寧(Ken-Ning Hsu)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 半母數擴充風險模型
(A Semiparametric Extended Hazards Model)
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摘要(中) 我們介紹一個半母數擴充風險模型 (semiparametric extended hazards model),它包含比例風險模型 (proportional hazards model) 和加速風險模型 (accelerated failure time model)。根據擴充風險模型這項特性,我們可以建立針對比例風險模型或加速風險模型的適合度檢定 (goodness-of-fit test)。另一方面,當比例風險模型和加速風險模型均不適合所描述的資料時,擴充風險模型提供了在模型選擇上的另外一種選擇。對具有與時間不相關風險因子的存活資料,我們藉由找出一核心平滑的簡化概似函數 (kernel-smoothed profile likelihood function) 的極大值對此擴充風險模型提出一個新的估計方法。透過一概似函數比值檢定 (likelihood ratio test)評估比例風險函數和加速風險函數建立一個適合度檢定。最後結果的估計量會被證明在大樣本時具有強烈一致性且服從常態分配,此外,也指明出概似比檢定統計量的分配。接著,我們考慮擴充風險模型的一推廣,其中允許風險因子的觀測值可以隨時間而改變。我們使用計數過程法 (counting processes approach) 重新建構擴充風險模型,並提出一不偏估計函數族 (a class of unbiased estimating function)。所得的估計量在大樣本以及適當條件下時具有一致性且服從常態分配。
摘要(英) We introduce a semiparametric extended hazards (EH) model which includes the proportional hazards (PH) model and the accelerated failure time (AFT) model as special cases. By the nested structure of the EH model, one can construct a goodness-of-fit test for the PH model or the AFT model. On the other hand, the EH model provides another choice of model for the given dataset when the PH model and the AFT model both fail to fit. For the survival data with time-independent covariates, we propose an estimation for the EH model by maximizing a kernel-smoothed profile likelihood function, and evaluate the goodness-of-fit of the PH model or the AFT model through a likelihood ratio test. The resulting estimators are proven to be strongly consistent and asymptotically normal, and the asymptotic distribution of the likelihood ratio test statistic is identified. Moreover, we consider an extension of the EH model in which the covariates are allowed to be time-dependent. We use the counting processes approach to reformulate the EH model and propose a class of unbiased estimating functions for the estimation in the EH model. The resulting estimators are proven to be consistent and asymptotically normal under regularity conditions.
關鍵字(中) ★ 加速風險模型
★ 擴充風險模型
★ 核心平滑的簡化概似函數
★ 概似比例檢定
關鍵字(英) ★ Accelerated failure time model
★ Likelihood ratio test
★ Proportional hazards model
★ Extended hazards model
★ Kernel-smoothed profile likelihood
論文目次 Contents
1 A semiparametric extended hazards model with time-independent covari-
ates 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Asymptotical properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12
1.4 Simulation studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15
1.5 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.5.1 Leukemia data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.5.2 Stanford heart transplant data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19
1.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2 A semiparametric extended hazards model with time-dependent covariates
23
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3 Asymptotical properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29
2.4 Variance estimation and goodness-of- t test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.5 Simulation studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36
2.6 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.6.1 Stanford heart transplant data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40
2.6.2 Taiwan AIDS data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
References 46
參考文獻 [1] Andersen, P. K. and Gill, R. D. (1982). Cox's regression model for counting processes: a large sample study. Annals of statistics 10: 1100-1120.
[2] Andersen P. K., Borgan Q., Gill R. D.and Keiding N. (1993). Statistical Models Based on Counting Processes. Springer: New York.
[3] Bickel, P. J. and Klaassen, C. A. J., Ritov, Y., Wellner, J. A. (1993). Efficient and Adaptive Estimation for Semiparametric Models. New York: Springer.
[4] Buckley, J. and James, I. (1979). Linear regression with censored data. Biometrika 66: 429-436.
[5] Chen, Y. Q. and Jewell, N. P. (2001). On a general class of semiparametric hazards regression models. Biometrika 88: 687-702.
[6] Ciampi, A. and Etezadi-Amoli, J. (1985). A general model for testing the proportional hazards and the accelerated failure time hypotheses in the analysis of censored survival data with covariates. Communication in Statistics-Theory and Methods. 14: 651-667.
[7] Cox, D. R. (1972). Regression models and life tables (with disscussion). Journal of the Royal Statistical Society, Series B 34: 187-220.
[8] Cox, D. R. (1975). Partial likelihood. Biometrika 62: 269-276.
[9] Cox, D. R. and Oakes, D. (1984). Analysis of Survival Data. London: Chapman & Hall.
[10] Crowley, J. and Hu, M. (1977). Covariance analysis of heart transplant survival data. Journal of the American Statistical Association 72: 27-36.
[11] Etezadi-Amoli, J. and Ciampi, A. (1987). Extended hazard regression for censored survival data with covariates: a spline approximation for the baseline hazard function. Biometrics 43: 181-192.
[12] Gehan, E. A. (1965). A generalized Wilcoxon test for comparing arbitrary single-censored samples. Biometrika 52: 203-223.
[13] Gill, R. D. and Schumacher M. (1987). A simple test of the proportional hazards assumption. Biometrika 74: 289-300.
[14] Grambsch, P. M. and Therneau, T. M. (1994). Proportional hazards tests and diagnostics based on weighted residuals. Biometrics 81: 515-526.
[15] Hsieh, F. (2001). On heteroscedastic hazards regression models: theory and application. Journal of the Royal Statistical Society, Series B 63: 63-79.
[16] Jin, Z., Lin, D. Y., Wei, L. J. and Ying, Z. (2003). Rank-based inference for the accelerated failure time model. Biometrika 90: 341-353.
[17] Jones, M. C. (1990). The performance of kernel density functions in kernel distribution function estimation. Statistics and Probability Letter 9: 129-132.
[18] Jones, M. C. and Sheather, S. J. (1991). Using non-statistic terms to advantage in kernel-based estimation of integrated squared density derivatives. Statistics and Probability Letter 11: 511-514.
[19] Lawless, J. (1982). Statistical Models and Methods for Lifetime Data. New York: Wiley.
[20] Lin, D. Y. and Geyer, C. J. (1992). Computational methods for semiparametric linear regression with censored data. Journal of Computational and Graphical Statistics 1: 77-90.
[21] Lin, D. Y. (1989). Goodness-of-fit tests and robust inference for the Cox proportional hazards model, unpublished Ph.D. dissertation, University of Michigan, Dept. of Biostatistics.
[22] Lin, D. Y. (1991). Goodness-of-fit analysis for the Cox regression model based on a class of parameter estimators. Journal of the American Statistical Association 86: 725-728.
[23] Lin, D. Y. and Ying, Z. (1995a). Semiparametric inference for the accelerated life model with time-dependent covariates. Journal of statistical Planning and Inference 44: 47-63.
[24] Lin, D. Y. and Ying, Z. (1995b). Semiparametric analysis of general additive-multiplicative hazard models for counting processes. The Annals of statistics 23: 1712-1734.
[25] Lin, D. Y., Wei L. J. and Ying, Z. (1998). Accelerated failure time models for counting processes. Biometrika 85: 605-618.
[26] Louis, T. A. (1981). Nonparametric analysis of an accelerated failure time model. Biometrika 68: 381-390.
[27] Miller, R. G. (1981). Survival Analysis. New York: Wiley.
[28] Murphy, S. A. and van der Vaart, A. W. (199 7). Semiparametric likelihood ratio inference. The Annals of statistics 25: 1471-1509.
[29] Nagelkerke, N. J. D., Oosting J. and Hart, A. A. M. (1984). A simple test for goodness of fit for Cox's proportional hazards model. Biometrics 40: 483-486.
[30] Nelder, J. A. and Mead, R. (1965). A simplex method for function minimization. The Computer Journal 7: 308-313.
[31] Parzen, M. I., Wei, L. J., and Ying, Z. (1994). A resampling method based on pivotal estimating functions.
Biometrika 81: 341-350.
[32] Prentice, R. L. (1978). Linear rank tests with right-censored data. Biometrika 65: 167-179.
[33] Ritov, Y. (1990). Estimation in a linear regression model with censored data. Annals of Statistics 18: 303-328.
[34] Robins, J. and Tsiatis, A. A. (1992). Semiparametric estimation of an accelerated failure time model with time-dependent covariates. Biometrika 79: 311-319.
[35] Schuster, E. F. (1969). Estimation of a probability density function and its derivatives. The Annals of Mathematical statistics, 40: 1187-1195
[36] Schoenfeld, D. (1980). Chi-squared goodness-of-fit tests for the proportional hazards regression model. Biometrika 67: 145-153.
[37] Schoenfeld, D. (1982). Partial residuals for the proportional hazards regression model. Biometrika 69: 239-241.
[38] Therneau, T.M. and Grambsch, P.M. (2000). Modeling Survival Data , New York: Springer.
[39] Tsiatis, A. A. (1981). A large sample study of Cox's regression model. Annals of Statistics 9: 93--108.
[40] Tsiatis, A. A. (1990). Estimating regression parameters using linear rank tests for censored data. Annals of Statistics 18: 354-372.
[41] van der Vaart, A. W. and Wellner, J. A. (1996). Weak Convergence and Empirical Processes. New York: Springer.
[42] van der Vaart, A. W. (1998). Asymptotic Statistics. London: Cambridge University Press.
[43] Wei, L. J. (1984). Testing goodness of fit for proportional hazards model with censored observations. Journal of the American Statistical Association 79: 649-652.
[44] Zeng, D. and Lin, D. Y. (2007). Efficient estimation for the accelerated failure time model. Journal of the American Statistical Association 102: 1387-1396.
指導教授 曾議寬(Yi-Kuan Tseng) 審核日期 2011-1-21
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