The Resource Semiparametric and nonparametric methods for the analysis of panel count data, by Yang Li

Semiparametric and nonparametric methods for the analysis of panel count data, by Yang Li

Label
Semiparametric and nonparametric methods for the analysis of panel count data
Title
Semiparametric and nonparametric methods for the analysis of panel count data
Statement of responsibility
by Yang Li
Creator
Contributor
Author
Thesis advisor
Subject
Genre
Language
eng
Summary
Panel count data are one type of event-history data concerning recurrent events. Ideally for an event-history study, subjects should be monitored continuously, so for the events that may happen recurrently over time, the exact time of each event occurrence is recordable. Data obtained in such cases are commonly referred to as recurrent event data (Cook and Lawless, 2007). In reality, however, subjects may only be observed at their clinical visits or discrete times. As a result, instead of observing the exact event times, one only knows the numbers of events that happen between the observation times. Such interval-censored recurrent event data are usually referred to as panel count data (Kalbfleisch and Lawless, 1985; Sun and Kalbfleisch, 1995; Thall and Lachin, 1988). The primary interest with panel count data is about the underlying recurrent event process. Meanwhile for the analysis, one needs to consider the times when the observations occur, which can be regarded as realizations of an observation process with follow-up times. This dissertation consists of four parts. In the first part, we will consider regression analysis of panel count data with dependent observation processes while the follow-up times may be subject to a terminal event like death. A semiparametric transformation model is presented for the mean function of the underlying recurrent event process among survivals. To estimate the regression parameters, an estimating equation approach is proposed and the inverse survival probability weighting technique is used. In addition, the asymptotic distribution of the proposed estimate is derived and a model checking procedure is presented. Simulation studies are conducted to evaluate finite sample properties of the proposed approach, and the approach is applied to a bladder cancer study. The second part will focus on regression analysis of multivariate panel count data in the presence of a terminal event. Both the observation process and the terminal event may be correlated with recurrent event processes of interest. We present a class of semiparametric additive models for the mean functions of the underlying recurrent event processes. For the estimation of the regression parameters, an estimating equation based inference procedure is developed. The asymptotic properties of the proposed estimators are established and a model-checking procedure is derived for practical situations. The third part will discuss nonparametric comparison based on panel count data. Most approaches that have been developed in the literature require an equal observation process for all subjects. However, such an assumption may not hold in reality. A new class of test procedures are proposed that allow unequal observation processes for the subjects from different treatment groups, and both univariate and multivariate panel count data are considered. The asymptotic normality of the proposed test statistics is established and a simulation study is conducted. The approach is applied to a skin cancer study. Finally, the last part will discuss some directions for future research
Cataloging source
MUU
http://library.link/vocab/creatorDate
1983-
http://library.link/vocab/creatorName
Li, Yang
Degree
Ph.D.
Dissertation note
Dissertation
Dissertation year
2013.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri--Columbia
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorDate
1961-
http://library.link/vocab/relatedWorkOrContributorName
Sun, Jianguo
http://library.link/vocab/subjectName
  • Statistics
  • Medical statistics
  • Biometry
  • Panel analysis
Label
Semiparametric and nonparametric methods for the analysis of panel count data, by Yang Li
Instantiates
Publication
Note
  • "May 2013."
  • "A Dissertation presented to the Faculty of the Graduate School the University of Missouri In Partial Fulfillment Of the Requirements for the Degree Doctor of Philosophy."
  • Dissertation supervisor: Dr. (Tony) Jianguo Sun
  • Vita
Bibliography note
Includes bibliographical references (pages 93-99)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Control code
891141405
Extent
1 online resource (viii, 115 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia.
Media type code
  • c
Other physical details
illustrations (some color)
Specific material designation
remote
System control number
(OCoLC)891141405
Label
Semiparametric and nonparametric methods for the analysis of panel count data, by Yang Li
Publication
Note
  • "May 2013."
  • "A Dissertation presented to the Faculty of the Graduate School the University of Missouri In Partial Fulfillment Of the Requirements for the Degree Doctor of Philosophy."
  • Dissertation supervisor: Dr. (Tony) Jianguo Sun
  • Vita
Bibliography note
Includes bibliographical references (pages 93-99)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Control code
891141405
Extent
1 online resource (viii, 115 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia.
Media type code
  • c
Other physical details
illustrations (some color)
Specific material designation
remote
System control number
(OCoLC)891141405

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