English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58239/91808 (63%)
Visitors : 13791412      Online Users : 44
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120027


    Title: Discrete time survival data with longitudinal covariates
    Authors: CC, Wen;YH, Chen
    Keywords: competing risks;measurement error;right‐censored data;semiparametric model;survival analysis
    Date: 2020-12-20
    Issue Date: 2021-03-05 12:11:18 (UTC+8)
    Abstract: Survival analysis has been conventionally performed on a continuous time scale. In practice, the survival time is often recorded or handled on a discrete scale; when this is the case, the discrete‐time survival analysis would provide analysis results more relevant to the actual data scale. Besides, data on time‐dependent covariates in the survival analysis are usually collected through intermittent follow‐ups, resulting in the missing and mismeasured covariate data. In this work, we propose the sufficient discrete hazard (SDH) approach to discrete‐time survival analysis with longitudinal covariates that are subject to missingness and mismeasurement. The SDH method employs the conditional score idea available for dealing with mismeasured covariates, and the penalized least squares for estimating the missing covariate value using the regression spline basis. The SDH method is developed for the single event analysis with the logistic discrete hazard model, and for the competing risks analysis with the multinomial logit model. Simulation results revel good finite‐sample performances of the proposed estimator and the associated asymptotic theory. The proposed SDH method is applied to the scleroderma lung study data, where the time to medication withdrawal and time to death were recorded discretely in months, for illustration.
    Relation: Statistics in Medicine 39(29), P.4372-4385
    DOI: 10.1002/sim.8729
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML11View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback