Methodologic Core

The Methodologic Core will be led by Paul Nietert, Ph.D., with Beth Wolf, Ph.D., serving as Associate Director. The core will include two Co-Investigators, Viswanathan Ramakrishnan, Ph.D., and Dongjun Chung, Ph.D. The core will provide services to ensure research data are of highest quality and provide novel biostatistical methods developed relevant to the CCCR.

Methodologic Core Leadership and Staff


Paul Nietert, Ph.D., Professor, Department of Public Health Sciences

As Director, Dr. Nietert will oversee all Methodologic Core functions, including the collaborations between the Methodologic Core and the Research Base investigators, novel biostatistical method development, educational outreach activities, and connecting the Research Base with other quantitative experts. He is an applied biostatistician with extensive experience in the field of rheumatology. He has served as principal investigator on other NIH- and AHRQ-funded research projects in the past, and currently leads the Methodology Core of the NIAMS-funded Multidisciplinary Clinical Research Center at MUSC. He also is Director of the Department of Public Health Sciences’ Collaborative Unit and Director of the Biostatistics Epidemiology and Research Design Program within the South Carolina Clinical and Translational Research (SCTR) Institute.


Beth Wolf, Ph.D., Assistant Professor, Department of Public Health Sciences

As Co-Director, Dr. Wolf will help Dr. Nietert oversee all aspects of the Methodologic Core. She will direct the innovations in biostatistical methods relevant to rheumatology and actively mentor junior faculty and research assistants. Having worked with Drs. Nietert and Ramakrishnan for the past 6 years, Dr. Wolf has been key to the success of our current CCCR (FORMERLY MCRC) Methodology Core and SCTR’s Biostatistics Epidemiology and Research Design Program. Her methods work in the area of decision trees has provided methodology for detecting and quantifying the relative importance of interactions, and in particular higher-order interactions (>2-way) among genetic and environmental factors for determining patient disease status. She has successfully applied these methods to evaluate interactions between smoke exposure and genetic factors with systemic lupus erythematosus, and in data examining associations between genetic and clinical factors with periodontal disease severity. She has more than 47 peer-reviewed publications, including a first author paper in Arthritis and Rheumatology and 8 methodologic articles in journals such as Bioinformatics and PloS One. She also mentors junior clinical faculty on a regular basis. Her current focus is on ensemble methods classifying longitudinal binary outcomes, such as SLE disease flares over time.


Viswanathan Ramakrishnan, Ph.D., Professor, Department of Public Health Sciences 

As a Co-Investigator, Dr. Ramakrishnanon will help with biostatistical collaborations, provide input on the novel methods development, and will help with educational outreach activities. He has been the Associate Director of the current CCCR (FORMERLY MCRC) Methodology Core and the SCTR Biostatistics Epidemiology and Research Design Program, and in the past has directed a cancer center’s biostatistics shared resource core (at Virginia Commonwealth University).


Dongjun Chung, Ph.D., Assistant Professor, Department of Public Health Sciences

Dr. Chung serves as a Co-Investigator on the Methodologic Core. He has a strong background in statistics, with specific training in statistical genomics and genetics. In addition to participating in relevant collaborative efforts, he will help lead the novel methods development. He was recently awarded an R21 research grant from NIH/NCI to develop a novel statistical framework for cancer subtype identification by integrating multiple genomic data types, pathway databases, and biomedical literature, all of which is translatable into rheumatologic disease. He also has developed a novel statistical framework for the integrative analysis of large GWAS datasets, incorporating various types of genomic and genetic data (e.g., DNase-seq and eQTL) to improve statistical power to identify genetic variants associated with disease risk. Recently, he also was awarded an R01 research grant from NIH/NIGMS to extend this statistical framework so that it can integrate GWAS data for a large number of phenotypes with high dimensional genomic annotation data.

Faculty Research Associate:

Laney Wilson, Statistician, Department of Public Health Sciences

As Faculty Research Associate, Ms. Wilson will assist Drs. Nietert and Wolf with data management, statistical programming, and preparation of reports and manuscripts. She is a doctoral level epidemiologist and has been an author of papers involving epilepsy, traumatic brain injuries, and survival analysis.