Biostatistics

Biostatistics

Research

Faculty Member   Research Areas  
Jaeun Choi, Ph.D.   Causal inference, comparative effectiveness research, survival analysis, correlated response, longitudinal data analysis
Charles Hall, Ph.D.   Parametric survival models with change points, Bayesian statistics
Moonseong Heo, Ph.D.   Mixed-effects modeling, cluster randomized clinical trials, sample size determination, meta-analysis, epidemiology of obesity
Mimi Kim, Sc.D.   Equivalence and non-inferiority trials; misclassification and measurement; interval-censored survival data, multivariate survival data
Ryung S. Kim, Ph.D.   Nested case-control studies, electronic health records, complex survey data, statistical genomics, Community health programs
Juan Lin, Ph.D.   High dimensional data analysis, cancer epidemiology
Yungtai Lo, Ph.D.   Mixture models, two-part models for longitudinal semi-continuous data
Jee-Young Moon, Ph.D.   Statistical genetics, epidemiologic methods
Wenzhu Mowrey,  Ph.D.   Imaging studies, high dimensional data analysis, survival and longitudinal data analysis.
Abdissa Negassa, Ph.D.   Tree-based methods, survival analysis, correlated data, omitted covariates, prognostic/predictive models, biomarker discovery, epidemiological methods
Kith Pradhan, Ph.D.   Bioinformatics, statistical genomics
Shankar Viswanathan, DrPH   Multivariate survival analysis, methods for analyzing missing data, agreement statistics
Cuiling Wang, Ph.D.   Missing data, analysis of longitudinal data, mediation analysis, ROC, survival analysis
Tao Wang, Ph.D.   Statistical genetics and genomics
Xianhong Xie, Ph.D.   Longitudinal data analysis; missing data; measurement error, survival analysis, imaging studies, smoothing splines
Xiaonan Xue,  Ph.D.   Survival analysis, longitudinal studies, cancer screening methods
Kenny Ye,  Ph.D.   Statistical modeling and data mining with high dimensional data; statistical genetics and genomics  

 

Grant Funding

For How Long is WTC Exposure Associated with Incident Airway Obstruction 

PI: C. Hall

National Institute for Occupational Safety and Health/Centers for Disease Control; 9/1/12-8/31/14

The study uses innovative statistical methods – parametric survival models with change points – to study the incidence of new onset obstructive airway disease (OAD) diagnoses and symptoms over the first ten years following World Trade Center exposure, with the goal of determining the length of time that exposure response gradients are observed among exposed FDNY firefighters. This study will allow estimation of the length of time that a relatively short-term, high intensity exposure may be associated with incident respiratory illness.

 

For How Long is WTC Exposure Associated with Chronic Rhinosinusitis 

PI: C. Hall

National Institute for Occupational Safety and Health/Centers for Disease Control; 7/1/14-6/30/16

This study continues the use of parametric survival models with change points to address a similar question in the incidence of chronic rhinosinusitis, another common respiratory condition associated with exposure to the World Trade Center rescue/recovery effort, with the goal of determining the length of time that exposure response gradients are observed among exposed FDNY firefighters.

 

An Integrated Analysis of Data from Multi-Center Trials in Lupus 

PI: M. Kim

Lupus Foundation of America; 2/1/10-10/30/16

In the past two decades, more than a dozen investigational products for lupus have entered phase II/III clinical trials and have failed. These trials have been burdened by the inherent heterogeneity of the disease and the variation in severity of symptoms. The goal of this project is to use statistical modeling approaches to identify predictors of disease outcomes in lupus patients randomized to the placebo arms of multiple clinical trials. The knowledge gained from this study will be used to design more efficient trials of future investigational agents.

 

Detecting Early Disease Using Variability in Markers Under Informative Censoring 

PI: C. Wang

NIH/National Institute of Aging;9/30/13-6/30/15.

The goal of this project is to develop time-dependent ROC approaches that utilize the heterogeneous variance in markers and take non-random censoring into account for detecting early disease.

 

Empirical-Bayesian Testing for Family Genome-Wide Association Data 

PI: T. Wang

NIH/National Human Genome Research Institute; 4/1/11-3/31/13

The goal of this study is to develop statistical approaches for complicated GWAS with both family and individual data.

 

Statistical Method for Identifying Genetic Modifiers of Conotruncal Heart Defects 

PI: T. Wang

NIH/National Heart, Lung, and Blood Institute; 8/1/13-04/30/15

The main goal is to develop novel statistical methodology for testing genetic association using a novel three-stage polynomial logistic regression model, which takes genetic heterogeneity among disease subtypes into account. The investigators plan to apply the proposed methodology to investigate genetic associations of structural cardiovascular malformations in 22q11DS children.

 

An Integrative Analysis of Structural Variation for the 1000 Genomes Project 

(PI subcontract: K. Ye)

NIH/National Human Genome Research Institute; 7/1/13-7/1/17

This study is to identify structural variations for the 1000 Genome Project.

 

New Methods to Uncover Global Transcriptional Programs for Disease Risk Variants 

PI: K. Ye

NIH/National Institute of General Medical Sciences; 4/1/13-3/31/17

The objective of this project is to investigate the link between the disease locus to alterations in global transcriptional programs by establishing the three-dimensional network of short- and long-distance genomic region interactions, the local and global alterations in gene transcription, and the key role of enhancer-RNA harboring risk alleles.

 

Understanding Genetic Basis of Autism 

PI: K. Ye

Simons Foundation (Subcontract from Cold Spring Harbor Laboratory); 1/1/12-03/31/14

The aim was to detect de novo mutations using sequencing technology and to understand the roles of de novo mutation and inherited rare variants in autism.

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