Biostatistics is the science of obtaining, analyzing and interpreting data in order to understand and improve human health. The mission of the Department of Biostatistics at the University of North Carolina at Chapel Hill is to forge advances in science that benefit human health through profound and paradigm-shifting innovations in biostatistical methodology and theory as well as the thoughtful implementation of biostatistical methods in practice.
Papers from 2016
Prevalence Estimation at the Cluster Level for Correlated Binary Data Using Random Partial-Cluster Sampling, Rujin Wang and John S. Preisser
Papers from 2015
Generalizing Evidence from Randomized Trials using Inverse Probability of Sampling Weights, Ashley L. Buchanan, Michael G. Hudgens, Stephen R. Cole, Katie Mollan, Paul E. Sax, Eric Daar, Adaora A. Adimora, Joseph Eron, and Michael Mugavero
Feature Elimination in Support Vector Machines and Empirical Risk Minimization, Sayan Dasgupta, Yair Goldberg, and Michael R. Kosorok
Papers from 2014
sanon : An R Package for Stratified Analysis with Nonparametric Covariable Adjustment, Atsushi Kawaguchi and Gary G. Koch
A Marginalized Zero-Inflated Negative Binomial Regression Model with Overall Exposure Effects, John S. Preisser, Kalyan Das, D. Leann Long, and John W. Stamm
Latent Supervised Learning for Estimating Treatment Effect Heterogeneity, Susan Wei and Michael R. Kosorok
Doubly Robust Learning for Estimating Individualized Treatment with Censored Data, Ying-Qi Zhao, Donglin Zeng, Eric B. Laber, Rui Song, Ming Yuan, and Michael R. Kosorok
Papers from 2013
Parameter Estimation in Cox Proportional Hazard Models with Missing Censoring Indicators, Naomi Brownstein, Eric Bair, Jianwen Cai, and Gary Slade
Cross-Validation for Nonlinear Mixed Effects Models, Emily Colby and Eric Bair
Feature Elimination in Empirical Risk Minimization and Support Vector Machines, Sayan Dasgupta, Yair Goldberg, and Michael R. Kosorok
Latent Supervised Learning, Susan Wei and Michael R. Kosorok
Latent Supervised Learning for Survival Data, Susan Wei and Michael R. Kosorok
Papers from 2012
Identification of biologically relevant subtypes via preweighted sparse clustering, Sheila Gaynor and Eric Bair
Reader Reaction: On Variance Estimation for the Fine-Gray Model, Chenxi Li, Robert J. Gray, and Jason P. Fine
A Multistage Non‐inferiority Study Analysis Plan to Evaluate Successively More Stringent Criteria for a Clinical Trial with Rare Events, Siying Li, Gary G. Koch, and Todd A. Schwartz DrPH
Change-Point Models to Estimate the Limit of Detection, Ryan C. May, Haitao Chu, Joseph G. Ibrahim, Michael G. Hudgens, Abigail C. Lees, and David M. Margolis
A Comparison of Methods for Generating Correlated Binary Variates with Specified Marginal Means and Correlations, John S. Preisser Jr. and Bahjat F. Qaqish
Research Methods for Clinical Trials in Personalized Medicine: A Systematic Review, Zheng Ren, Marie Davidian, Stephen L. George, Richard M. Goldberg, Fred A. Wright, Anastasios A. Tsiatis, and Michael R. Kosorok
Developing Adaptive Personalized Therapy for Cystic Fibrosis using Reinforcement Learning, Yiyun Tang and Michael R. Kosorok
Empirical Pathway Analysis, without Permutation, Yi-Hui Zhou, William T. Barry, and Fred A. Wright
Simple and accurate trend tests using a permutation approximation, Yi-Hui Zhou and Fred A. Wright
Reinforcement Learning Trees, Ruoqing Zhu, Donglin Zeng, and Michael R. Kosorok
Papers from 2011
ORTH: R and SAS Software for Regression Models of Correlated Binary Data Based on Orthogonalized Residuals and Alternating Logistic Regressions, Kunthel By, Bahjat F. Qaqish, John S. Preisser, Jamie Perin, and Richard C. Zink
Causal Inference for Vaccine Effects on Infectiousness, M. Elizabeth Halloran and Michael Hudgens
Deletion Diagnostics for Alternating Logistic Regressions, John S. Preisser, Kunthel By, Jamie Perin, and Bahjat F. Qaqish
Papers from 2010
The Interactive Decision Committee for Chaemical Toxicity Analysis, Chaeryon Kang, Hao Zhu, Fred A. Wright, Fei Zou, and Michael R. Kosorok
Group Testing for Case Identification with Correlated Responses, Samuel D. Lendle, Michael Hudgens, and Bahjat F. Qaqish
Randomization-Based Inference within Principal Strata, Tracy L. Nolen and Michael Hudgens
Partial Proportional Odds Models for Longitudinal Ordinal Data, John S. Preisser, Ceib Phillips, Jamie Perin, and Todd A. Schwartz
Papers from 2009
Inverse Regression Estimation for Censored Data, Nivedita V. Nadkarni, Yingqi Zhao, and Michael R. Kosorok
Graphical Displays for Clarifying How Allocation Ratio Affects Total Sample Size for the Two Sample Logrank Test, Benjamin R. Saville, Yong S. Kim, and Gary G. Koch
Variable Selection by Bayesian Adaptive Lasso and Iterative Adaptive Lasso, with Application for Genome-wide Multiple Loci Mapping, Wei Sun, Joseph G. Ibrahim, and Fei Zou
Reinforcement Learning Design for Cancer Clinical Trials, Yufan Zhao, Michael R. Kosorok, and Donglin Zeng
Reinforcement Learning Strategies for Clincal Trials in Non-small Cell Lung Cancer, Yufan Zhao, Michael R. Kosorok, Donglin Zeng, and Mark A. Socinski
Papers from 2008
A Simulation Experiment to Investigate the Distributional Behavior of Extreme Cook's Distance for GEE to Models with Contaminated Binary Responses, Kunthel By, John Preisser, and Bahjat Qaqish
Performance of One-Step Approximation Relative to Exact Cluster Cook's Distance for GEE, John Preisser, Kunthel By, and Bahjat Qaqish
Testing Variance Components in Multilevel Linear Models using Approximate Bayes Factors, Benjamin R. Saville, Amy H. Herring, Jay S. Kaufman, and David A. Savitz
Analyzing Correlated Longitudinal and Survival Data in Clinical Trials Using Multivariate Time-to-Event Methods, Benjamin R. Saville, Amy H. Herring, and Gary G. Koch
Papers from 2007
Applications of Extensions of Bivariate Rank Sum Tests to the Bilateral Crossover Design to Compare Two Treatments Through Four Sequence Groups, Atsushi Kawaguchi, Gary G. Koch, and Ratna Ramaswamy
Testing random effects in the linear mixed model using approximate Bayes factors, Benjamin R. Saville and Amy H. Herring
On Asymptotically Optimal Tests Under Loss of Identifiability in Semiparametric Models, Rui Song, Michael R. Kosorok, and Jason P. Fine
Regression Models for Identifying Noise Sources in Magnetic Resonance Images, Hongtu Zhu, Yimei Li, Joseph G. Ibrahim, Xiaoyan Shi, Hongyu An, Yasheng Chen, Weili Lin, Daniel B. Rowe, and Bradley G. Peterson