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The Research Design and Statistics Core:
- Conducts methodological studies applicable to social, behavioral and health care research.
- Provides statistical support for DUSON’s research through collaborating and consulting activities with faculty members, postdocs and graduate students.
- Teaches graduate courses on research methods and statistics.
Evidence-based practice has become a common practice in health care, and evidence has to be revealed through data analysis. Our core can help DUSON’s research community:
- Identify and develop appropriate analysis techniques.
- Further inform DUSON researchers with advanced statistical methods for them to continue to foster innovative research so as to move DUSON’s research forward.
Statistical Collaboration and Consultation
Collaboration implies that statisticians and clients work together to make decisions regarding the design of studies, the collection and analysis of data and the presentation and dissemination of research findings. If you need statistical collaboration, please read the general guidelines and submit a statistical support request (accessed through CNR Research Tools).
Consultation implies that statisticians provide methodological and statistical advice and guidance to clients interested in making decisions regarding the design of studies, the collection and analysis of data and the presentation and dissemination of research findings. If you need statistical consultation, please attend a statistics lab session.
Research Design and Statistics Lab
The Research Design and Statistics Lab provides walk-in, ad hoc consultation related to research design and statistical analysis for DUSON faculty, students, postdocs and DUHS nurses for their research with regard to the following needs:
- Questionnaire design
- Measurement validation
- Database design
- Data management
- Data analysis
- Statistical software coding
The Lab is located in the Pearson Building, Room 3143. The hours are Mondays from 1:00 p.m. to 4:00 p.m. and Thursdays from 9:00 a.m. to 12:00 p.m. Walk-ins are welcome.
- Methodological Research
- Moderation and mediation
- Survival analysis on competing risks data
- Aging and longitudinal modeling
- Causal inference
- Propensity score analysis
- Dynamic Treatment Regime and SMART design
- Statistical Support
- Grant proposal preparation
- Research design
- Questionnaire construction
- Measurement validation
- Data analysis plan
- Power analysis and sample size determination
- Data management
- Data analysis and statistical modeling
- Manuscript preparation
- Training & Education
- PhD Program
- N915 Measurement & Theory Practice
- N911 Statistical Methods & Data Analysis
- N903 General Linear Models
- N904 Categorical Data Analysis
- N905 Longitudinal Method & Analysis
- N9XX Special Topics
- DNP Program
- N966 Quantitative Evaluation
- MSN Program
- Nurse as Scholar I
- SAS workshops
- PhD Program
- Publications and presentations
Faculty and Staff Bios
Dr. Wei Pan is Associate Professor and Director of the Research Design and Statistics Core at Duke University School of Nursing. He received his PhD in measurement and quantitative methods from Michigan State University in 2001. Dr. Pan’s research work focuses on causal inference, longitudinal analysis, meta-analysis, psychometrics and their applications in the social, behavioral and health sciences. He recently published an edited book on Propensity Score Analysis, a statistical technique to approximate randomized controlled trials (RCTs) by reducing selection bias in non-RCT or observational data so as to increase the validity of causal inference from non-RCTs or observational studies.
Dr. Qing Yang received her BS in mathematics from Beijing Institute Technology in China and her MS and PhD in biostatistics from the University of California, Los Angeles. Dr. Yang’s statistical research interest focuses on longitudinal data analysis, adaptive treatment regime, SMART design and survival analysis. As a biostatistician, she has extensive experience collaborating with researchers in different therapeutic areas, including smoking cessation, depression, diabetes, cardiovascular disease, breast cancer, mobile health and more.
Dr. Christian Douglas received her doctoral training in biostatistics at the University of North Carolina Gillings School of Global Public Health. Her dissertation research focused on examining the effects of mortality on the statistical methods for analyzing non-mortality health outcomes in a study of older adults. Prior to joining the Research Design and Statistics Core, Christian worked with many different health professionals on their research, including a geriatric dentist, an injury prevention epidemiologist and a social epidemiologist. Christian loves fitness (boot camp, Zumba ®, Xtend Barre ®, running and hiking), experiencing new cuisines, tutoring, science-fiction/fantasy novels and movies, traveling and playing games (crossword puzzles, Sudoku and Scrabble).
Dr. Jianhong Chang earned her PhD in cell biology and molecular genetics from University of Maryland, College Park, and her MS in statistics from North Carolina State University. She had extensive biomedical research experience prior to joining the Research Design and Statistics Core. Her professional experience and research interests include observational studies, design of experiment, regression models, data management, machine learning and big data. With a multidisciplinary background in statistics and genetics, she brings with her an enthusiastic pursuit of leveraging multiple source data (e.g., genetic, clinical, behavioral, social and environmental) via statistics and data science to advance nursing research.
Sathya Amarasekara is a statistician (III) in the School of Nursing at Duke University. He received his MS in statistics from the University of Texas at El Paso in 2014. As a statistician in the School of Nursing at Duke University and a research associate at Texas Tech University Health Sciences Center El Paso, he has assisted principal investigators of research projects to streamline and perform productive research by functions related to statistical analysis and research databases. He has managed and worked in the main domains of statistical analysis and programming (SAS, R) and research-related databases (REDCap and EMR).