在競爭性風險與左截取資料之累積發生函數的半參數估計

Tunghai University Institutional Repository > 管理學院 > 統計學系所 > 碩博士論文 > Item 310901/1120

Please use this identifier to cite or link to this item: http://140.128.103.80:8080/handle/310901/1120

Title:	在競爭性風險與左截取資料之累積發生函數的半參數估計
Other Titles:	Semiparametric Estimation of the Cumulative Incidence Function Under Competing Risks and Left-truncated Sampling
Authors:	楊易蓁 Yang, Yi-Chen
Contributors:	沈葆聖 Shen, Pao-Sheng 東海大學統計學系
Keywords:	最大概似估計 EM algorithm;Maximum likelihood estimation;Incidence probabilities.
Date:	2009
Issue Date:	2011-02-18T07:15:54Z (UTC)
Abstract:	假設(T1; T2)是我們有興趣的兩競爭風險的存活時間, V和(T3;C)分別表示左截取和右設限變數, 其中T3是與兩競爭風險獨立的事件,而V和C 是相關的,P(C > V ) = 1.Huang and Wang (1995)已經提出兩種估計累積發生函數的方法,一個是左截取分配沒有任何假設且包含右設限變數的無母數最大概似估計;令一個則是假設左截取是length-biased但不包含右設限的半參數估計,我們加上第三個獨立的競爭風險,且將其推廣至左截取右設限的資料型態;最後利用模擬比較無母數和半參數兩種方法的累積發生函數 Let (T1; T2) denote the lifetimes of two competing risks of interest and can be dependenton each other. Let V and (T3;C) denote left truncation and right censoring variables, respectively,where C denotes the censoring time due to termination of the follow-up period, andT3 is the censoring time due to a failure other than the two failures of interest, which mightoccur before the cross-section time. Assume that (T1; T2), (V;C) and T3 are independent ofone another other but V and C are dependent with P(C _ V ) = 1. Let aG denote the leftsupport of V . In application, due to truncation, some a > aG is selected so that the size ofthe observed risk set at a is not too small. Let _1(xja) = P(T1 _ x; T1 _ T2jT1 > a; T2 > a)and _2(xja) = P(T2 _ x; T2 _ T1jT1 > a; T2 > a) denote the conditional cumulative incidencefunction of T1 and T2, respectively. When V is uniformly distributed and C = T3 = 1,Huang and Wang (1995) proposed a semiparametric estimator of _k(xj0). In this article, weextend previous models by considering the case when the distribution of V is parameterizedas G(x; _) and the distributions of Tk and C are left unspeci_ed. Several iterative algorithmsare proposed to obtain the semiparametric estimates (denoted by _k(x; ^_nja)) of _k(xja).The consistency of _k(x; ^_nja) is derived. Simulation results show that the semiparametricestimators _k(x; ^_nja) have smaller mean squared errors compared to the nonparametricmaximum likelihood estimator of _k(xja).
Appears in Collections:	[統計學系所] 碩博士論文

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	429	View/Open

Loading...