Kesheng Wang, PhD

Professor

Organization:

West Virginia University School of Nursing

Department:

Family / Community Health Department

Classification:

Faculty

• PhD, Georg-August-University of Goettingen, 2001
• MA, China Agricultural University, 1992
• BS, Henan Normal University, 1989

Wang, K., Shafique, S., Wang, N., Walter, S. M., Xie, X., Piamjariyakul, U., & Winstanley, E. (2023) Early-onset alcohol, tobacco, and illicit drug use with age at onset of hypertension: a survival analysis.. Social psychiatry and psychiatric epidemiology.

Wang, K., Theeke, L. A., Liao, C., Wang, N., Lu, Y., Xiao, D., & Xu, C. (2023) Deep learning analysis of UPLC-MS/MS-based metabolomics data to predict Alzheimer’s disease. (Vol. 453, p. 120812). Journal of the neurological sciences.

Wang, K., Shafique, S., Xiao, D., Walter, S. M., Liu, Y., Piamjariyakul, U., & Xie, C. (2023) Repeated measures analysis of opioid use disorder treatment on clinical opiate withdrawal scale in a randomized clinical trial: sex differences. (pp. 1–12). Journal of addictive diseases.

Dai, Z., & Wang, K. (2023) The association between early onset of alcohol, smokeless tobacco and marijuana use with adult binge drinking in United States. (Vol. 13, p. 187). Scientific reports.

Wang, K., DiChiacchio, T., Fang, W., Lander, L. R., Feinberg, J., Xie, C., … Piamjariyakul, U. (2022) Longitudinal study of impact of medication for opioid use disorder on Hamilton Depression Rating Scale. (Vol. 297, pp. 148–155). Journal of affective disorders.

• WVU Provost's Research Most Valuable Faculty Players Award - (April, 2021)

Dr. Kesheng Wang is a Professor in Biostatistics at the School of Nursing. Before joining WVU, Dr. Wang was an Associate Professor in the Department of Biostatistics and Epidemiology, East Tennessee State University. Dr. Wang has developed/applied advanced statistical methods including linear and non-linear mixed effect models, longitudinal data analysis, survival models, multivariate data analysis (such as principal component regression, structural equation modeling, feature selection, machine learning, and deep learning etc), meta-analysis, and Bayesian methods. Dr. Wang has considerable expertise in using R packages, SAS, SAS callable SUDAAN, and SPSS/AMOS in the analyses of large datasets in clinical trials, national surveys, and genetic studies. Furthermore, Dr. Wang has been involved in funded research grants in epidemiological and genetic studies of alcohol dependence and drug abuse, Alzheimer disease, cancer, diabetes, heart disease, hypertension, stroke, HCV and HIV, and schizophrenia. Since 2003, Dr. Wang has published over 170 peer-reviewed scientific papers; while he has served as an editorial member for several journals in biometrics/biostatistics, epidemiology and public health, medical genetics/genomics, and statistical genetics. Dr. Wang earned his B.S. in Biology. Through graduate studies (a M.A. and a Ph.D.) he obtained advanced training in Biostatistics, Population and Quantitative Genetics. Especially, Dr. Wang received 4 years postdoctoral training in Biostatistics (including Genetic Epidemiology and Statistical Genetics) at the Hospital for Sick Children and University of Toronto, Canada.

Teaching interests: Health Research Statistics I and II, Mixed Models in Clinical Trials and Survey Data, Multivariate Analysis, and Survival Models

RESEARCH INTERESTS

Biostatistics and Artificial Intelligence (AI) and Health Informatics: Linear and generalized linear mixed models; longitudinal data analysis; survival models; data mining and machine learning/deep learning [cluster analysis, principal component analysis, structural equation modeling (SEM), feature selection and LASSO, random forest, XGBoost, support vector machines (SVMs), gradient boosting, and deep learning etc.]; multi-level analysis; Bayesian method; Big Data analysis and health Informatics; biostatistics in clinical trials, EHR, large survey, and healthcare data

Aging and Substance Use and Mental Health: Epidemiology of alcohol and other drugs (AOD); mental health (anxiety, depression/major depressive disorder, psychological distress, sleep, and schizophrenia); Alzheimer’s disease; aging and dementia; aging and cardiovascular diseases; epigenetic clocks and cognitive performance; health behaviors

Cancer Screening and Epidemiology: Health behaviors (alcohol and drug use, smoking, and activity), nutrition factors (diet and nutrition), mental health (anxiety, depression, and psychological factors), and beliefs about cancer with cancer screening and cancer epidemiology

Bioinformatics/Genetic Epidemiology/Statistical Genetics/Statistical Genomics: Statistical methods and machine learning/deep learning in omics studies of complex diseases including alcohol and drug use disorders, Alzheimer’s disease, cancer, cardiovascular diseases, mental health, HCV and HIV; genome-wide association study (GWAS); microbiome x environment interaction; meta-analysis; epigenomics, proteomics and metabolomics in ageing and Alzheimer’s disease; statistical methods in animal models

CURRENT RESEARCH TOPICS

Health behaviors and beliefs with cancer screening and telehealth: Using factor analysis, structural equation modeling, mixed models and machine learning (ML) tools to investigate the impact of health behaviors, nutrition factors, health care, and mental health with cancer screening and telehealth using the Health Information National Trends Survey (HINTS) datasets and the Behavioral Risk Factor Surveillance System (BRFSS) data

Longitudinal study in drug abuse treatment in the multi-center clinical trials: Linear and generalized linear mixed models, survival models, and machine learning (ML) methods in the longitudinal study of drug abuse treatment using the NIDA's National Drug Abuse Treatment Longitudinal Clinical Trials Network (CTN) datasets

Substance use disorders and mental disorders: Mixed model, multi-level analysis, principal component regression and SEM, Poisson regression and machine learning (ML) tools in early alcohol, drug and tobacco use, and psychological factors with adult cardiovascular diseases, mental disorders and substance use disorders using the National Survey on Drug Use and Health (NSDUH) datasets

Biomarkers and cognitive performances in Alzheimer’s disease, dementia, and aging-related phenotypes: Longitudinal studies in aging, Alzheimer’s disease, dementia, cardiovascular diseases, and related cognitive phenotypes using the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Health and Retirement Study (HRS) data

Machine learning- and deep learning-based integrative analysis of multi-omics data: Genome-wide association study (GWAS), proteomic, metabolomic, DNA methylation, RNA expression data, and imaging data (DTI, MRI, fMRI and PET) in Alzheimer’s disease and ageing-related phenotypes using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data

• Testing the Mediating Effect of Mood State on PAP Use and Health-related Quality of Life: Secondary Analysis of APPLES Data - Ruth and Robert Kuhn Nursing Faculty Research Award - 2023 - 2024
• Determining factors related to family home caregivers’ burden and health status - Benedum Palliative Care Funding - 2023 - 2024
• Reducing Stress and Improving Resilience and Self-Compassion of Nursing Students During a Pandemic - West Virginia University Faculty Research and Scholarship Advancement - 2021 - 2022
• Genome-wide association study of cerebrospinal fluid inflammation biomarkers in Alzheimer disease - V914 Skaggs Family Dean's Special Projects - 2020 - 2021
• Home Care Needs of Caregivers and Patients with Heart Failure and Dementia in Rural Appalachia - NIH R15 - 2020 - 2023
• Mechanisms of Alzheimer Disease from the Connection Point of View - Virginia Pyne Kaneb Faculty Scholars Program Grant - 2020 - 2021
• Coaching End-of-Life Palliative Care (EOLPC) for End-Stage Heart Failure Patients and Their Family Caregivers in Rural Appalachia - NIH/NIGMS/NINR R15 - 2019 - 2023