Search Results for "Hui-Yi Lin"

Hui-Yi Lin named Program Director of Biostatistics

Hui-Yi Lin, Ph.D., MS has been named Director of the Biostatistics Program at the Louisiana State University Health Sciences (LSUHSC) School of Public Health. She is a Professor of Biostatistics at LSUHSC and also serves as the Director of the Biostatistics & Bioinformatics Core at the Center for Translational Viral Oncology (CTVO), and as Co-Director of the Data Analytical Unit of the LSUHSC-NO Comprehensive Alcohol-HIV/AIDS Research Center (CARC). Her extensive background in genetic statistics, machine learning, and statistical applications in public health, cancer, and medical-related fields makes her a perfect fit for the Director’s role at LSUHSC. Her primary research interest is developing novel statistical methods for genetic studies. In addition to her work at LSUHSC, she actively participates in many projects in the international prostate cancer genome consortium.

Currently, she is PI for a multiple-year Department of Defense (DoD) grant for developing genetic risk scores for prostate cancer. She is also a Co-Investigator for several NIH grants. She has ~180 peer-review publications in high-impact journals. Furthermore, Dr. Lin has profound working experience working for ~50 NIH grants at 2 NCI-designated comprehensive cancer centers: Moffitt Cancer Center and Research Institute and Comprehensive Cancer Center at the University of Alabama at Birmingham (UAB). Check her full CV HERE.

Hui-Yi Lin

Curriculum Vitae: HLIN1.CV.PDF

Study Finds Flushing Water Lines Protects Inconsistently and May Increase Lead Exposure

Research conducted by LSU Health New Orleans School of Public Health finds that the current recommendations for running water to flush out lead are not consistently effective and may not be the best way to protect children from lead in drinking water. The findings are published in the International Journal of Environmental Research and Public Health available online here.

“While flushing taps according to prevailing guidelines (for 30 seconds to 2 minutes) may reduce water lead levels for some homes, over half the tested homes had peak water lead levels after that time, so these recommendations may inadvertently increase exposures,” notes study leader Adrienne Katner, DEnv, Assistant Research Professor of Environmental & Occupational Health Sciences at LSU Health New Orleans School of Public Health. “More effective interventions like certified water filters should be considered instead, particularly when replacing water service lines and plumbing is not economically possible.”

The research team surveyed homeowners and tested water samples from 376 New Orleans homes on the East Bank of the Mississippi River (the city’s water source) between February 2015 and November 2016. Recruited homes met criteria for the potential presence of lead. Virginia Tech colleagues analyzed the water samples, which were first cold water draws, first hot water draws, draws after letting the water run for 30 – 45 seconds, 2.5 – 3 minutes or 5.5 – 6 minutes.

Overall, New Orleans water lead levels were typically low compared to the EPA’s action level of 15 ppb. The analysis revealed a water lead level of 5 ppb in 88% of all samples from normal-use residential sites. More than half of all samples from normal use sites (60%) had detectable water lead levels of at least 1 ppb or higher. Water lead levels ranged from non-detectable (less than 1 ppb) to 58 ppb found in samples after flushing for 30-45 seconds. There was no significant difference in water lead level distribution after flushing for the various amounts of time until the six-minute mark after which the water lead level distribution decreased.

“While the percent of samples with water lead levels exceeding 1 ppb did decrease after extended flushing for 5.5 to 6 min, the reductions were not always substantial,” says Dr. Katner. “If the aim is to prevent childhood lead exposure altogether, preferably, or at least reduce it to the minimally detectable level (1 ppb) as recommended by the American Academy of Pediatricians, then New Orleans may require more proactive interventions than flushing to meet this goal. We could not verify that a one-time flush is sufficient to maintain low water lead levels. Some studies evaluating flushing at school taps suggest frequent flushes may be needed throughout the day, as waterborne lead can return to pre-flush levels within hours. Prolonged and repeated flushing may also not be practical, cost-effective, or sustainable over the long term, especially in cities with declining water resources and/or rising water rates.”

Katner and her colleagues conclude, “Public health messages should be modified to ensure appropriate application of flushing, while acknowledging its short-comings and practical limitations.”

In addition to Katner, the LSU Health New Orleans research team included Drs. Hui-Yi Lin, Chih-Yang Hu, as well as Komal Brown and Xinnan Wang. Researchers from Virginia Tech, Tulane University, and Corona Environmental Consulting also participated.The research was supported in part by the Louisiana Board of Regents’ New Research Pilot Funding Program, the National Science Foundation, LSU Health New Orleans School of Public Health and the National Institutes of Health.


Research Projects

Dr. Hui-Yi Lin’s primary research interest is developing novel statistical methods for genetic studies and omics data analyses, especially interactions of genetic variants for cancers. She also works in big data analysis and machine learning fields. Moreover, Dr. Lin has profound statistical consultation experience with health professionals across multiple disciplines, such as basic science, health behavioral studies, epidemiology studies, and clinical trials. She serves as the Director of the Biostatistics & Bioinformatics Core at the Center for Translational Viral Oncology (CTVO) and as Co-Director of the Data Analytical Unit of the LSUHSC-NO Comprehensive Alcohol-HIV/AIDS Research Center (CARC) at LSUHSC. She is the Principal Investigator (PI) for a multiple-year Department of Defense (DoD) grant for developing polygenic risk scores (PRSs) for prostate cancer. She also served as PI for several NIH grants (such as R21) and as a Co-Investigator for several NIH grants. She has more than 175 peer-reviewed publications in high-impact journals. In addition to her work at LSUHSC, she actively participates in the international prostate cancer genome consortium. Dr. Lin also has extensive working experience at the 2 NCI-designated comprehensive cancer centers: Moffitt Cancer Center and Research Institute and Comprehensive Cancer Center at the University of Alabama at Birmingham (UAB).

# Key words: Genetic Statistics In Cancers, Omic Data Analysis, Big Data Analysis, Machine Learning, and Clinical Trial 

Dr. Qingzhao Yu serves as the interim Associate Dean at the School of Public Health. Currently, she holds the role of Contact Principal Investigator for two significant NIH grants: An R01 project titled “Interactions between ES-miRNAs and environmental risk factors are responsible for TNBC progression and associated racial health disparities: a novel analysis with multilevel moderation inferences” and an R15 project titled “Trends of disparities in breast cancer progression and health care considering multilevel risk factors.” Dr. Yu has been the Principal Investigator, co-Investigator, or biostatistician on numerous grants and projects. She has been authored in more than 130 peer-reviewed publications, one book, and four statistical software packages. Her research primarily focuses on advancing statistical methods for causal inferences and high-dimensional data analysis. These methodologies have found widespread application in uncovering mechanisms underlying observed health disparities, assessing interventions and health policies, as well as optimizing experimental designs. 

# Key words: Bayesian Method, Machine Learning and Data Mining Techniques, Mediation Analysis, Optimal Experimental Designs, and Spatial Data Analysis.

Dr. Donald Mercante‘s research interests include design and analysis of experiments, multivariate permutation methods, and statistical models for correlated data. He is the principal investigator for the NIH-funded Data Management and Analysis Core for the LSU Superfund Research Center and the Co-Director of the Biostatistics and Epidemiology Research Design Core for the NIH-funded LaCATS Center grant at LSU-HSC. 

# Keywords: Clinical Trials, Study Design, Permutation Methods, and Correlated Data

Dr. Zhide Fang’s research interests include (1) developing novel statistical methodologies for analyzing high-throughput, large-scale genomic, epigenomic metagenomics data; (2)  developing efficient algorithms for Big Data; (3) developing methods and algorithms for constructing model-based experimental designs for the development of statistical theory.

# Key words: Gene Expression, DNA Methylation, Microbiome, Big Data, and Machine Learning

Dr. Evrim Oral’s statistical methodology research involves several different areas. She specifically focuses on proposing robust estimation in generalized linear models (GLM) and survey sampling. She also develops novel estimators that utilize randomized response techniques (RRT) for sensitive topics. Her methodology research also includes proposing new estimation techniques and statistical tests for data subject to limit of detection (LOD). She does research on several other areas of survey sampling as well, such as ranked set sampling and assessing the effects of unit nonresponse and attrition in health-related studies. Her collaborative research interests are diverse. She has been working as a Co-investigator on several NIH-funded projects such as Quality of Life in Prostate Cancer Project (QPCaP) and The Women and Their Children’s Health (WaTCH). She worked for the LSU Health Care Services Division for many years and utilized big data from electronic health records (EHR) to improve LSU patients’ outcomes. She has been providing statistical consulting and supporting research in many departments and schools of LSUHSC, such as OB/GYN, Otolaryngology, Emergency Department, and School of Nursing. She is currently doing research with the Comprehensive Alcohol-HIV/AIDS Research Center (CARC) researchers and evaluating the effects of alcohol use in people with HIV.

# Keywords: Generalized Linear Models, Robust Statistics, Survey Methodology, Surveying Sensitive Topics, and Level of Detection

Biostatistics & Data Science (BSDS)

The Biostatistics and Data Sciences program at our School of Public Health has a clear educational mission: to train students in statistical, computational, and data science methods to apply the methods to tackle intricate challenges within public health and biomedical research. Our program is designed to foster a profound comprehension of statistical theory and data science methods for Ph.D. students while also emphasizing the practical applications of biostatistics and data science in public health research and practice for MS, MPH, and Ph.D. students.

By completing our program, graduates acquire a diverse skill set that enables them to design and conduct research and clinical studies, analyze complex datasets, interpret results, and effectively communicate findings to various audiences, including researchers, policymakers, and the general public. Additionally, our graduates contribute to the formulation of policies and guidelines concerning the utilization of data in healthcare and public health. Our program prepares students for a wide range of career opportunities. Over the years, our graduates have secured positions in local and federal government entities, such as the Department of Education of Louisiana, prestigious research institutes like the National Institutes of Health, universities, hospitals, and industries, including insurance companies and pharmaceutical firms.

Furthermore, our program is actively involved in research endeavors and community outreach initiatives aimed at advancing the field of biostatistics and data sciences while striving to improve public health outcomes. We contribute to the advancement of health and disease understanding by developing and applying statistical methods and data science techniques.

We also engage in collaborations with researchers from diverse disciplines, assisting them in extracting meaningful insights from complex datasets and developing novel statistical methodologies for emerging areas in biomedicine. Notably, our faculty members lead biostatistics centers/cores such as the Comprehensive Alcohol-HIV/AIDS Research Center, Center for Translational Viral Oncology, LSU Superfund Data Management and Analysis Core, and Louisiana Clinical and Translational Science Center. Additionally, our faculty members secure external research funding as Principal Investigators for esteemed programs like the National Institutes of Health (NIH) and the Department of Defense (DoD) for developing and applying new statistical methods for breast cancer and prostate cancer.

Overall, the primary mission of our biostatistics and data sciences department is to enhance human health by conducting rigorous statistical analysis and data interpretation. Through our research activities and collaborations, we strive to contribute to the ongoing progress in the field, ultimately making a positive impact on public health.

Lin, Hui-Yi Professor & Interim Program Director
Fang, Zhide Professor
Mercante, Donald Professor
Oral, Evrim Associate Professor
Yu, Qingzhao Professor & Interim Associate Dean for Research
Program contact:

Hui-Yi Lin, PhD
Interim Program Director and Professor
Phone: 504-568-6083; Email:

Impact of Dietary Quality on Genital Oncogenic HPV Infection in Women

Hui-Yi Lin, Qiufan Fu, Tung-sung Tseng, Xiaodan Zhu, Krzysztof Reiss, L Joseph Su, Michael E Hagensee

Most cervical cancers are directly linked to oncogenic or high-risk human papillomavirus (HR-HPV) infection. This study evaluates associations between diet quality and genital HPV infection in women.

This study included 10,543 women from the 2003–2016 National Health and Nutrition Examination Survey. The outcome was the genital HPV infection status (HPV-negative, low-risk [LR] HPV, and HR-HPV). Dietary quality was evaluated using the Healthy Eating Index (HEI), with which a higher score indicates a better diet quality.

Women who are not consuming total fruits (15.8%), whole fruits (27.5%), or green vegetables and beans (43%) had a significantly higher risk of HR-HPV infection than women who complied with the Dietary Guidelines for Americans (HR-HPV OR = 1.76, 1.63 and 1.48 for a HEI score of 0 vs. 5) after adjusting confounding factors. Similar results of these food components on LR-HPV infection were shown. In addition, intake of whole grains and dairy was inversely associated with LR-HPV infection.

Read the full article HERE.

Spring 2019

Spring 2019

Speaker: Daniel Fort, PhD
Affiliation: Ochsner Health System, Center for Applied Health Services Research, New Orleans-LA
Title: Unsupervised Learning: Clustering
Date and Time: January 28, 2019, 3:00 pm
Room: LEC, Room 303 

Speaker: Peter F Thall, PhD
Affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
Title: Statistical Remedies for Flawed Conventions in Medical Research
Date and Time: February 11, 2019, 3:00 pm
Room: LEC, Room 303

Speaker: Chuck Huber, PhD
Affiliation: StataCorp and Texas A&M School of Public Health
Title: Casual Inference for Complex Observational Data
Date and Time: February 18, 2019, 3:00 pm
Room: LEC, Room 303

Speaker: Angela Stodghill, PhD
Affiliation: Janssen R & D (Johnson & Johnson), Raritan-NJ
Title: Responsibilities and Required Skills of Clinical Statisticians in Clinical Trials Processes
Date and Time: March 15, 2019, 10.00 am
Room: LEC, Room 303

Speaker: Michelle Lacey, PhD
Affiliation: Tulane University, Department of Mathematics, New Orleans-LA
Title: Modeling Methylation Patterns with Long Read Sequencing Data
Date and Time: March 18, 2019, 3:00 pm
Room: LEC, Room 303

Speaker: Hui-Yi Lin, PhD
Affiliation: LSUHSC, School of Public Health, Biostatistics Program, New Orleans-LA
Title: Impact of Interaction patterns on genetic association studies: an example of prostate cancer
Date and Time: April 1, 2019, 3:00 pm
Room: LEC, Room 303

Speaker: Christian Geneus, MS, MPH
Affiliation: Tulane School of Public Health and Tropical Medicine, Department of Global Biostatistics and Data Science, New Orleans-LA
Title: Quantifying Uncertainty in Model Predictions: Stages of the Network Modeling
Date and Time: April 8, 2019, 3:00 pm
Room: LEC, Room 303

Speaker: Tat Yau, MSc
Affiliation: Louisiana State University Health Sciences Center, Biostatistics Program, New Orleans-LA
Title: Inference in Linear Multivariate Envelope Model
Date and Time: April 10, 2019, 2:00 pm
Room: MEB, Room S3 (3212)

Speaker: Yaling Li
Affiliation: Louisiana State University Health Sciences Center, Biostatistics Program, New Orleans-LA
Title: A Simulation Study to Check the Consequences of Violating Assumptions in Mediation Analysis
Date and Time: April 18, 2019, 8:30 am
Room: MEB, Room S7 (3224)

Speaker: Sungsu Kim, PhD
Affiliation: University of Louisiana at Lafayette, Department of Mathematics, Lafayette-LA
Title: A Multivariate Circular Distribution with Applications to the Protein Structure Prediction Problem
Date and Time: April 22, 2019, 3:00 pm
Room: LEC, Room 303

Speaker: Huaizhen Qin, PhD
Affiliation: University of Florida, Department of Epidemiology, Gainesville-Florida
Title: Rare Variant Association Mapping in Admixed Populations
Date and Time: April 29, 2019, 3:00 pm
Room: LEC, Room 303

Organizer: Evrim Oral, PhD

LSU: Faculty and Researchers Used Emergency Department Data to Estimate Prevalence of Smoking in Young Adults

Faculty and researchers at the LSU School of Medicine (Drs. Stephen Kantrow and Sarah Jolley), Louisiana State University Health Sciences Center School of Public Health (Ms. Xinnan Wang, Dr. Tung-Sung Tseng, Dr. Dodie Arnold, Dr. Claudia Leonardi, Dr. Richard Scribner, Dr. Edward Trapido, Dr. Hui-Yi Lin), Ochsner Health System (Eboni Price Haywood) and the Louisiana Public Health Institute (Dr. Lisanne Brown) used emergency department (ED) data to estimate the prevalence of smoking in young adults. Most state or national surveys of smoking are limited in size- especially when looking at county level data, and phone based surveys have had decreasing responses. School based surveys are helpful, but limited by age.

The researchers used data from electronic health records (EHRs) for five EDs within U.S. Census-defined metropolitan New Orleans (New Orleans–Metairie, LA) for persons 18-24 years old. (15 percent of young adults are reported to have used EDs in the past year). Smoking status was available for 55,777 persons (91 percent of the total Emergency Departments); 61 percent were women, 55 percent were black, 35 percent were white, and 8 percent were Hispanic. One third of patients were uninsured. Most smokers used cigarettes (95 percent ). Prevalence of current smoking was 21.7 percent for women and 42.5 percent for men. Smoking prevalence was highest for substance use disorder (58 percent ), psychiatric illness (41 percent ) and alcohol use (39 percent ), and lowest for pregnancy (13.5 percent ). In multivariable analyses, male gender, white race, lack of health insurance, alcohol use, and illicit drug use were independently associated with smoking. Smoking risk among alcohol and drug users varied by gender, race, and/or age.

The BRFSS estimated 29 percent prevalence during the same time, and had data on 597 subjects aged 18–30 years. Although ED data are likely to overestimate tobacco use, the large sample size is useful- especially for stratum-specific estimates- particularly in a demographically diverse population. Dr. Stephen Kantrow, the lead investigator, stated ”this approach provides smoking data for a large sample of young adults in one metropolitan area, and may support longitudinal studies of smoking in high and low risk populations.

Full article

Better Statistical Methods to Understand Gene Interactions Leading to Cancer Development

(504) 568-4806; CELL (504) 452-9166

New Orleans, LA – Research led by Hui-Yi Lin, PhD, Associate Professor of Biostatistics at  LSU Health New Orleans School of Public Health, has developed another novel statistical method for evaluating gene-to-gene interactions associated with cancer and other complex diseases. The Additive-Additive 9 Interaction (AA9int) method is described in a paper published in Bioinformatics, available online here.

“This method can identify combinations of genetic variants for predicting cancer risk and prognosis,” notes Dr. Lin, who is also the paper’s lead author.

AA9int is based upon another method Lin developed, SNP Interaction Pattern Identifier (SIPI), to identify interactions between single nucleotide polymorphisms (SNPs). According to the National Institutes of Health, “Single nucleotide polymorphisms, frequently called SNPs (pronounced “snips”), are the most common type of genetic variation among people. Each SNP represents a difference in a single DNA building block, called a nucleotide.  Most commonly, these variations are found in the DNA between genes. They can act as biological markers, helping scientists locate genes that are associated with disease. When SNPs occur within a gene or in a regulatory region near a gene, they may play a more direct role in disease by affecting the gene’s function.”

Although SNP-SNP or gene-gene interaction studies have been emerging, the statistical methods for evaluating SNP-SNP interactions are still in their infancy. The conventional approach to test SNP interactions is to use a hierarchical interaction model with two main effects plus their interaction with both SNPs as an additive inheritance mode. However, this approach tests just one specific type of interaction, which can lead to many false negative findings.

SNP Interaction Pattern Identifier (SIPI), the first statistical method to thoroughly search for meaningful SNP-SNP interaction patterns in cancer and other complex diseases, can detect novel SNP interactions that the conventional statistical approach cannot. SIPI evaluates 45 SNP interaction patterns. Its computational demands are large, however, which may not be desirable for large-scale studies. So, Lin and her colleagues sought a smaller version with fewer testing models but with similar power. They showed that a mini version of SIPI – AA9int, which is composed of nine interaction models – used only about 20% of computing time. More efficient and feasible for large-scale studies, AA9int is still more effective than the traditional approach.

“We found that AA9int successfully detected 72-90% of the SIPI-identified SNP pairs,” reports Lin. “Not meant to replace SIPI, but for large-scale studies, AA9int is a powerful tool that can be used alone or as the screening stage of a two-stage approach (AA9int+SIPI) to detect SNP-SNP interactions.”

The research team also studied the impact of inheritance mode and model structure on detecting SNP-SNP interactions. SNP Interaction Pattern Identifier (SIPI) evaluates SNP interaction patterns by considering three major factors: model structure (hierarchical and non- hierarchical model), genetic inheritance mode (dominant, recessive and additive), and mode coding direction. AA9int considers non-hierarchical model structure and the additive mode. They found that non-hierarchical models play a more important role in SNP interaction detection than inheritance modes.

“These identified gene-gene or SNP-SNP interactions increase our understanding of the biological mechanisms of cancer development and may improve cancer diagnosis accuracy and reduce cancer-related deaths in the future.” Lin concludes.

The research team included scientists from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) Consortium.

Computational facilities at LSU Health New Orleans School of Public Health were supported by high-performance computational resources provided by the Louisiana Optical Network Infrastructure (LONI).

The research was supported by a grant from the National Cancer Institute of the National Institutes of Health.


APHA Live! Available for On-Demand

Couldn’t make it to APHA 2017 in Atlanta?
You can still watch on-demand sessions and presentations that you missed – learn, be inspired APHA Live.
Contact  for the LSU password to access the on-demand sessions of APHA for Faculty, Students and Staff.

13th Annual Public Health Finance Roundtable
Sunday, November 5th, 2017 Georgia World Congress Center – Room A405 3:00pm to 5:30pm
Click here for more information

APHA Film Festival: Screening of LSU Sponsored film “Michelle’s Story”
Session FF12: Global Public Health Film Festival: Inform, Educate, Empower, session 4 scheduled for Tuesday, November 7, 2017: 6:30 p.m.-8:00 p.m. 

Individual Presentations:

  • Oral Presentation
    “Expanding Patient Navigation Services in the Louisiana Breast and Cervical Health Program”
    Courtney S. Wheeler, MPH, Nannozi Ssenkoloto, MPH, Joann Lee, MPH, Donna Williams, DrPH
  • Poster Presentation
    “Drowned Out: The Smoke-Free East Baton Rouge Campaign and the Impact of a Thousand Year Flood”
    Authors: Aubree Thelen, MPH, Mikal Giancola, MPH, Lydia Kuykendal, MPH, Tonia Moore
  • Poster Presentation
    “Gender And Age Disparities In Relationship Of Acculturation, Sugar-sweetened Beverages Consumption And Obesity Among Latino Immigrants”
    Authors: Tung-Sung Tseng, DrPH, Shuang Yang, MS, Daesy K. Behrhorst, BA , Yu-Wen Chiu, DrPH, Chih-Yang Hu, MSPH, ScD , Hui-Yi Lin, PhD