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Sequential analysis of clustered survival data by marginal methods
Descriptive
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Title
Sequential analysis of clustered survival data by marginal methods
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Hou
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Bo
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1961-
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Bo Hou
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author
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Sackrowitz
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Harold
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Advisory Committee
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Harold Sackrowitz
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chair
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Zhang
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Cunhui
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Advisory Committee
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Cunhui Zhang
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internal member
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Xie
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Minge
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Advisory Committee
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Minge Xie
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internal member
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Liu
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Mengling
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Advisory Committee
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Mengling Liu
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outside member
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Ying
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Zhiliang
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Advisory Committee
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Zhiliang Ying
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Rutgers University
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Graduate School - New Brunswick
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theses
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2008
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2008-01
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English
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electronic
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ix, 60 pages
Abstract
Clustered survival data are a type of multivariate survival data with naturally formed clusters so that event times within a cluster are parallel to each other and correlated. Lee, Wei and Amato (1992) introduced a semiparametric model for the analysis of clustered survival data that assumes event times follow a proportional hazards model.
Sequential analysis of clustered survival data arises in clinical studies in which patients are followed over time and interim analyses are performed. This thesis studies the sequential analysis of clustered survival data with staggered patient entry by adapting Lee, Wei and Amato's approach. It is shown that the two-parameter score process converges to a Gaussian random field irrespective of the correlation within clusters and staggered patient entry. The regression parameter estimator obtained at each time point has the desired properties including consistency and asymptotic normality. A consistent estimator of the baseline cumulative hazard function is also given. More importantly, we propose a novel optimal weighting strategy. We show that the resulting score process not only produces a more efficient estimator, but also has the important property of (asymptotically) independent increments. The latter can be used in conjunction with the well-known error-spending functions to construct proper boundaries in group sequential testing. Finally a sample size calculation formula is given for designing clinical
trials with clustered survival time as the endpoint.
Sequential analysis of clustered survival data arises in clinical studies in which patients are followed over time and interim analyses are performed. This thesis studies the sequential analysis of clustered survival data with staggered patient entry by adapting Lee, Wei and Amato's approach. It is shown that the two-parameter score process converges to a Gaussian random field irrespective of the correlation within clusters and staggered patient entry. The regression parameter estimator obtained at each time point has the desired properties including consistency and asymptotic normality. A consistent estimator of the baseline cumulative hazard function is also given. More importantly, we propose a novel optimal weighting strategy. We show that the resulting score process not only produces a more efficient estimator, but also has the important property of (asymptotically) independent increments. The latter can be used in conjunction with the well-known error-spending functions to construct proper boundaries in group sequential testing. Finally a sample size calculation formula is given for designing clinical
trials with clustered survival time as the endpoint.
Note
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Ph.D.
Note
(type = bibliography)
Includes bibliographical references (p. 50-52).
Subject
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Topic
Statistics and Biostatistics
Subject
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Topic
Survival analysis (Biometry)
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17140
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ETD_680
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doi:10.7282/T3KW5GDD
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ETD doctoral
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Rights
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Copyright protected
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Open
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Bo Hou
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Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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