November 11, 2:00-3:00pm, 796 COE
Professor Sanjib Basu,
Department of Mathematics, Northern Illinois University
A unified competing risks cure rate model with application to cancer survival data
A competing risks framework refers to multiple risks acting simultaneously on
a subject or on a system. A cure rate, or a limited-failure model, postulates a fraction of the subjects/systems
to be cured or failure-free, and can be formulated as a mixture model, or alternatively by a bounded cumulative
hazard model. We develop models that unify the competing risks and limited-failure approaches.
The identifiability of these unified models are studied in detail. We describe Bayesian analysis of these models,
and discuss conceptual, methodological and computational issues related to model fitting and model selection.
We describe detailed applications in survival data from breast cancer patients in the Surveillance,
Epidemiology, and End Results (SEER) program of the National Cancer Institute (NCI) of the United States.
November 4, 2:00-3:00pm, 796 COE
Professor Yi Li,
Department of Biostatistics, University of Michigan
A New Class of Estimating Equation-based Variable Selectors for Risk Assessment
We propose a new class of estimating equation-based Dantzig selectors that can achieve simultaneous estimation and variable selection in the absence of a likelihood function, even when the number of covariates exceeds the number of samples. Our research was motivated by practical problems encountered in two studies: a clinical trial of therapies for head and neck cancer, and a genomics study of multiple myeloma patients. These problems proved difficult to analyze under the likelihood setting and must instead be approached with estimating equations. We prove nonasymptotic probability bounds on the accuracy of our estimator, report extensive simulation results, and use our method to analyze the aforementioned problems and construct more accurate prediction rules.
(Joint work with Dave Zhao from Harvard University)
October 21, 2:00-3:00pm, 796 COE
Assistant Professor Sarah Henes,
Division of Nutrition,
Byrdine F. Lewis School of Nursing and Health Professions,
Georgia State University
Future Research and Collaboration
Previous data indicate that measuring resting energy expenditure with a portable indirect calorimeter in the clinical setting is a particularly important assessment tool in older obese youth (Henes, unpublished data, 2010). While childhood obesity may be leveling off on the national level, recent data indicates that 17% of youth aged 2 to 19 years of age that are overweight and obese in the state of Georgia. To date, childhood obesity is one of the most dire public health dilemmas of our time. Registered dietitians and health care professionals can better determine caloric targets for weight loss in this population with the use of a clinically useful tool such as indirect calorimetry.
The primary objective of this grant proposal is to access funds to purchase a portable indirect calorimeter. A pilot study will then be implemented such that resting energy expenditure (MREE) will be measured in obese teen youth (aged 17-18 yrs. of age) using the ReeVue portable indirect calorimeter used in the clinical setting and compared with MREE measured in the research setting with a metabolic cart. This validation study will then be used to help implement the use of portable indirect calorimetry in the clinical and community setting. One of the goals is to provide clinicians with a more accurate measurement of energy needs which can then be used to determine more accurate caloric targets for weight loss in the treatment of childhood obesity
For pilot study: recruit 17-18 year old college freshman who are at or above the 85th percentile for age and gender. Sample size will be determined by power statistics. For further study- collaborations with community and medical center initiatives for the prevention and treatment of childhood obesity are currently being investigated.
October 14, 2:00-3:00pm, 796 COE
Associate Professor Ming Yuan,
Industrial and Systems Engineering,
Georgia Institute of Technology
High dimensional inverse covariance matrix estimation
Abstract: More and more often in practice, one needs to estimate a high dimensional covariance matrix. In this talk, we discuss how this task is often related to the sparsity of the inverse covariance matrix. In particular, we consider estimating a (inverse) covariance matrix that can be well approximated by ``sparse'' matrices, which oftentimes connects with graphical models. Taking advantage of such connection, we introduce an estimating procedure that can effectively exploit such ``sparsity''. The proposed methods can be efficiently computed and have the potential to be used in very high dimensional problems. Oracle inequalities are established for the estimation error in terms of several operator norms,
showing that the methods are adaptive to different types of sparsity of the problem.
October 7, 2:00-3:00pm, 796 COE
Assistant Professor Yuanhui Xiao,
Department of Mathematics and Statistics,
Georgia State University
Missing data is a common complication in data analysis. Missing data can cause difficulties in estimation, precision and inference. Although there are many methods to deal with incomplete data, multiple imputation (MI) has become one of the leading methods. In this talk, I will give an overview of MI and the theory behind it. I will also go over some softwares for MI and discuss the challenges for MI.