淡江大學機構典藏:Item 987654321/100178
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/100178


    Title: Regression analysis of mixed recurrent-event and panel-count data
    Authors: Zhu, Liang;Tong, Xinwei;Sun, Jianguo;Chen, Man-Hua;Deo Kumar Srivastava;Wendy Leisenring;Leslie L. Robison
    Contributors: 淡江大學統計學系
    Keywords: Estimating equation-based approach;Maximum likelihood approach;Regression analysis
    Date: 2014-07
    Issue Date: 2015-02-05 10:00:03 (UTC+8)
    Publisher: Oxford: Oxford University Press
    Abstract: In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation.
    Relation: Biostatistics 15(3), p.555-568
    DOI: 10.1093/biostatistics/kxu009
    Appears in Collections:[Graduate Institute & Department of Statistics] Journal Article

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