Abstract

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.

Public Health Relevance

We propose Bayesian and frequentist methodology for local and case 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 models. The proposed methodology is very useful in applications involving chronic diseases, such as cancer and AIDS.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA074015-12
Application #
7896590
Study Section
Special Emphasis Panel (ZRG1-HOP-T (02))
Program Officer
Dunn, Michelle C
Project Start
1997-09-01
Project End
2012-06-30
Budget Start
2010-07-01
Budget End
2012-06-30
Support Year
12
Fiscal Year
2010
Total Cost
$205,494
Indirect Cost

  Institution

Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599

  Publications

Ankerst, Donna P; Goros, Martin; Tomlins, Scott A et al. (2018) Incorporation of Urinary Prostate Cancer Antigen 3 and TMPRSS2:ERG into Prostate Cancer Prevention Trial Risk Calculator. Eur Urol Focus :
Joeng, Hee-Koung; Chen, Ming-Hui; Kang, Sangwook (2016) Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application. Lifetime Data Anal 22:38-62
Fraser, Raphael André; Lipsitz, Stuart R; Sinha, Debajyoti et al. (2016) Approximate median regression for complex survey data with skewed response. Biometrics 72:1336-1347
Rao, Shangbang; Ibrahim, Joseph G; Cheng, Jian et al. (2016) SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging. J Comput Graph Stat 25:1195-1211
Lipsitz, Stuart R; Fitzmaurice, Garrett M; Arriaga, Alex et al. (2015) Using the jackknife for estimation in log link Bernoulli regression models. Stat Med 34:444-53
M'lan, Cyr Emile; Chen, Ming-Hui (2015) Objective Bayesian Inference for Bilateral Data. Bayesian Anal 10:139-170
Guo, Ruixin; Ahn, Mihye; Zhu, Hongtu et al. (2015) Spatially Weighted Principal Component Analysis for Imaging Classification. J Comput Graph Stat 24:274-296
Gao, Qibing; Ahn, Mihye; Zhu, Hongtu (2015) Cook's Distance Measures for Varying Coefficient Models with Functional Responses. Technometrics 57:268-280
Sun, Qiang; Zhu, Hongtu; Liu, Yufeng et al. (2015) SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression. J Am Stat Assoc 110:289-302
de Castro, Mário; Chen, Ming-Hui; Zhang, Yuanye (2015) Bayesian path specific frailty models for multi-state survival data with applications. Biometrics 71:760-71

Showing the most recent 10 out of 112 publications