|Grant Number:||5R01CA074015-12 Interpret this number|
|Primary Investigator:||Ibrahim, Joseph|
|Organization:||Univ Of North Carolina Chapel Hill|
|Project Title:||Inference in Regression Models with Missing Covariates|
In this proposal, we propose Bayesian and frequentist methodology for local influence diagnostics and develop model assessment tools for complete data settings as well as in the presence of missing covariate and/or response data for a variety of statistical models, including generalized linear models, models for longitudinal data, and survival model. In Specific Aim 1, we develop frequentist local influence measures and goodness of fit statistics based on the general local influence development of Cook (1986), and discuss these measures for i) linear models with missing at random (MAR) and nonignorably missing covariates and ii) generalized linear models with MAR and nonignorably missing covariates. For Specific Aim 2, we develop new classes of Bayesian case influence diagnostics for the complete data setting then generalize these diagnostics to the missing data framework. The proposed methodologies in Aims 1-2 are primarily motivated from several studies in the PI's collaborative work.
Bayesian dynamic regression models for interval censored survival data with application to children dental health.
Authors: Wang X, Chen MH, Yan J
Source: Lifetime Data Anal, 2013 Jul;19(3), p. 297-316.
EPub date: 2013 Feb 7.
ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions.
Authors: Rashid NU, Giresi PG, Ibrahim JG, Sun W, Lieb JD
Source: Genome Biol, 2011 Jul 25;12(7), p. R67.
EPub date: 2011 Jul 25.
Choosing among partition models in Bayesian phylogenetics.
Authors: Fan Y, Wu R, Chen MH, Kuo L, Lewis PO
Source: Mol Biol Evol, 2011 Jan;28(1), p. 523-32.
EPub date: 2010 Aug 27.
Local influence for generalized linear models with missing covariates.
Authors: Shi X, Zhu H, Ibrahim JG
Source: Biometrics, 2009 Dec;65(4), p. 1164-74.
A note on the validity of statistical bootstrapping for estimating the uncertainty of tensor parameters in diffusion tensor images.
Authors: Yuan Y, Zhu H, Ibrahim JG, Lin W, Peterson BS
Source: IEEE Trans Med Imaging, 2008 Oct;27(10), p. 1506-14.
Bayesian error-in-variable survival model for the analysis of GeneChip arrays.
Authors: Tadesse MG, Ibrahim JG, Gentleman R, Chiaretti S, Ritz J, Foa R
Source: Biometrics, 2005 Jun;61(2), p. 488-97.
Wavelet thresholding with bayesian false discovery rate control.
Authors: Tadesse MG, Ibrahim JG, Vannucci M, Gentleman R
Source: Biometrics, 2005 Mar;61(1), p. 25-35.
Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology.
Authors: Tadesse MG, Ibrahim JG, Mutter GL
Source: Biometrics, 2003 Sep;59(3), p. 542-54.
Non-ignorable missing covariates in generalized linear models.
Authors: Lipsitz SR, Ibrahim JG, Chen MH, Peterson H
Source: Stat Med, 1999 Sep 15-30;18(17-18), p. 2435-48.
Using missing data methods in genetic studies with missing mutation status.
Authors: Leong T, Lipsitz SR, Ibrahim JG
Source: Stat Med, 1999 Feb 28;18(4), p. 473-85.