Grant Number:	5R01CA075142-11 Interpret this number
Primary Investigator:	Kosorok, Michael
Organization:	Univ Of North Carolina Chapel Hill
Project Title:	Semiparametric and Empirical Process Methods in Oncology
Fiscal Year:	2008

DESCRIPTION (provided by applicant): Project Summary: The long-term goal of the proposed project is to develop flexible semi-parametric analysis tools for clinical, epidemiological and basic science studies in oncology. Semi-parametric models are potentially very useful in medical research because they involve both a parametric component which is usually easy to interpret and a nonparametric component which permits greater flexibility in the presence of biologic uncertainty. While these models are highly compelling scientifically, the limited availability of user-friendly inferential techniques for such models has severely restricted their use in practice. Part of the reason for this limitation is that the increased flexibility of the models adds an order of magnitude to the difficulty of the inference. Addressing this gap in inferential methodology is the central goal of the proposed research. This goal will be accomplished through achieving the following four aims: (1) Develop and evaluate more flexible and effective statistical analysis methods for transformation models in right censored time-to-event data; (2) Develop and evaluate tools for flexible semi-parametric risk-factor assessment in interval censored cancer studies; (3) Create tools for computationally efficient inference in semi-parametric models for cancer research; and (4) Develop flexible, semi-parametric methods for the analysis of extremely high dimensional screening data for cancer studies. While the application areas of these aims seems diverse, all of the aims involve a combination of semi-parametric model inference and empirical process methods. Thus the statistical underpinnings of the aims are highly interrelated. Relevance: Moreover, these aims will provide diverse cancer researchers with a significantly improved collection of scientifically meaningful, flexible, and user- friendly data analysis tools.




Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer.
Authors: Zhao Y. , Zeng D. , Socinski M.A. , Kosorok M.R. .
Source: Biometrics, 2011 Dec; 67(4), p. 1422-33.
PMID: 21385164
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Inverse regression estimation for censored data.
Authors: Nadkarni N.V. , Zhao Y. , Kosorok M.R. .
Source: Journal Of The American Statistical Association, 2011-03-01 00:00:00.0; 106(493), p. 178-190.
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Simultaneous Critical Values For T-Tests In Very High Dimensions.
Authors: Cao H. , Kosorok M.R. .
Source: Bernoulli : Official Journal Of The Bernoulli Society For Mathematical Statistics And Probability, 2011 Feb; 17(1), p. 347-394.
PMID: 21572564
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Detection of gene pathways with predictive power for breast cancer prognosis.
Authors: Ma S. , Kosorok M.R. .
Source: Bmc Bioinformatics, 2010-01-01 00:00:00.0; 11, p. 1.
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Reinforcement learning design for cancer clinical trials.
Authors: Zhao Y. , Kosorok M.R. , Zeng D. .
Source: Statistics In Medicine, 2009-11-20 00:00:00.0; 28(26), p. 3294-315.
PMID: 19750510
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On Asymptotically Optimal Tests Under Loss of Identifiability in Semiparametric Models.
Authors: Song R. , Kosorok M.R. , Fine J.P. .
Source: Annals Of Statistics, 2009 Oct; 37(5A), p. 2409-2444.
PMID: 20454527
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What's So Special About Semiparametric Methods?
Authors: Kosorok M.R. .
Source: Sankhya. Series B. [methodological.], 2009-08-01 00:00:00.0; 71-A(2), p. 331-353.
PMID: 20640048
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Rejoinder on Discussion of: What's So Special About Semiparametric Methods?
Authors: Kosorok M.R. .
Source: Sankhya. Series B. [methodological.], 2009-08-01 00:00:00.0; 71-A(2), p. 369-371.
PMID: 20640050
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Identification of differential gene pathways with principal component analysis.
Authors: Ma S. , Kosorok M.R. .
Source: Bioinformatics (oxford, England), 2009-04-01 00:00:00.0; 25(7), p. 882-9.
EPub date: 2009-04-01 00:00:00.0.
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The Penalized Profile Sampler.
Authors: Cheng G. , Kosorok M.R. .
Source: Journal Of Multivariate Analysis, 2009-03-01 00:00:00.0; 100(3), p. 345-362.
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On semiparametric efficient inference for two-stage outcome-dependent sampling with a continuous outcome.
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Source: Biometrika, 2009-01-26 00:00:00.0; 96(1), p. 221.
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On Brownian Distance Covariance and High Dimensional Data.
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Source: The Annals Of Applied Statistics, 2009-01-01 00:00:00.0; 3(4), p. 1266-1269.
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Robust covariate-adjusted log-rank statistics and corresponding sample size formula for recurrent events data.
Authors: Song R. , Kosorok M.R. , Cai J. .
Source: Biometrics, 2008 Sep; 64(3), p. 741-50.
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Robust semiparametric microarray normalization and significance analysis.
Authors: Ma S. , Kosorok M.R. , Huang J. , Xie H. , Manzella L. , Soares M.B. .
Source: Biometrics, 2006 Jun; 62(2), p. 555-61.
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Additive risk models for survival data with high-dimensional covariates.
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Source: Biometrics, 2006 Mar; 62(1), p. 202-10.
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A sample size formula for the supremum log-rank statistic.
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Source: Biometrics, 2005 Mar; 61(1), p. 86-91.
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Design and analysis of group sequential clinical trials with multiple primary endpoints.
Authors: Kosorok M.R. , Yuanjun S. , DeMets D.L. .
Source: Biometrics, 2004 Mar; 60(1), p. 134-45.
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Simultaneous inferences on the contrast of two hazard functions with censored observations.
Authors: Gilbert P.B. , Wei L.J. , Kosorok M.R. , Clemens J.D. .
Source: Biometrics, 2002 Dec; 58(4), p. 773-80.
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Exact simultaneous confidence bands for a collection of univariate polynomials in regression analysis.
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Source: Statistics In Medicine, 1999-03-15 00:00:00.0; 18(5), p. 613-20.
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