BRIAN BARGER, Ph.D.
Brian Barger is the Quantitative Lead at the Center for Leadership in Disability housed within the Mark Chaffin Center for Healthy Development, where he conducts research and evaluation on multiple disability related projects. His primary research interests include socio-emotional development and measurement in individuals with autism spectrum conditions (ASC), developmental screening, early signs and identification of autism and developmental disabilities, and mental health screening.
MATT HAYAT, Ph.D.
Matt Hayat is a biostatistician with more than 20 years of experience on a host of health-related research studies in a wide array of health and disease areas. He has collaborated with physicians, nurse scientists, statisticians, epidemiologists, and other healthcare workers and researchers. His collaborative efforts have included substantial involvement in the design, conduct, and analysis of randomized trials, and he teaches the graduate course PH8885 Fundamentals of Clinical Trials. He is currently a Co-Investigator on four federally funded clinical trials.
- Statistics collaboration in the health sciences
- Statistics education of health professionals
- Study design and planning
- Design, conduct, and analysis of clinical trials
- Issues related to the use of statistical inference and interpretation of its findings
- Data analysis, interpretation, and statistical reporting
- Development and application of advanced statistical methods for analyzing complex or correlated health data
BETTY LAI, Ph.D.
Betty Lai is a methodologist and clinical researcher. Her collaborations have included examinations of mental and physical health outcomes for children and families, child abuse, abnormal development, continuous quality improvement, agency evaluations, and intervention development. Dr. Lai’s research has been funded by the National Science Foundation. In recognition of her work, Division 56, the Trauma Division of the American Psychological Association, awarded Dr. Lai with an Early Career Award.
RUIYAN LUO, Ph.D.
Ruiyan Luo is a statistician with collaborative experience spanning a wide range of topics in the fields of public health, biology, nutrition, and kinesiology and health. She uses modern statistical methods and develops novel statistical methods to address complex data arising from her collaborations with health scientists. Her methodology research focus is on addressing the difficulties with small sample sizes relative to the number of predictors, as well as situations where explanatory and/or response variables are discrete time observations from curves.
- Bayesian data analysis, hierarchical modeling, and applications
- High dimensional data analysis when the sample size is small and the number of predictors is large: sparse methods for principal component analysis, partial least square, regression and classification
- Longitudinal data analysis, Functional data analysis with functional predictors and/or responses
KATHERINE MASYN, Ph.D.
Katherine Masyn is a biostatistician and prevention science methodologist with over 15 years of experience as a statistician on federally-funded research grants across a wide range of topics in the fields of public health, psychology, and education, utilizing a vast array of modern statistical analysis techniques. She possesses a strong commitment to the effective and accessible dissemination of emerging statistical methodology to substantive researchers and has taught, individually and as part of a team, numerous trainings and workshops, both nationally and internationally. Her own methods research focuses on the use of latent variables to model population heterogeneity in cross-sectional and longitudinal settings.
- Discrete- and continuous-time survival analysis with latent variables
- Latent growth (mixture) models for continuous and categorical outcomes
- Joint models for multiple and multi-faceted longitudinal processes
- Cross-sectional, longitudinal, and multilevel latent class and finite mixture models
- Measurement invariance and differential item functioning in latent class and latent transition analysis
- Factor mixture models: Hybrid latent variable models with latent factors and classes
- Multilevel factor analysis and structural equation modeling
- The intersection of propensity score analysis with latent variable modeling in cross-sectional and longitudinal studies observational studies
- Bayesian structural equation modeling
SCOTT WEAVER, Ph.D.
Scott Weaver is a quantitative methodologist and prevention scientist with over 10 years of experience as a statistician and investigator on projects spanning topics such as normative and atypical developmental trajectories of immigrant and minority youth, health disparities, urban health, HIV, tobacco, community interventions, and substance use/abuse.
- Statistical hypothesis testing and statistical inference
- Generalized Linear Models
- Latent variable models (including structural equation models)
- Multilevel & longitudinal models
- Psychometrics (including classical test theory, factor analysis, latent class analysis, measurement invariance)
- Mediation/moderation analysis
- Finite mixture models for person-centered analysis
- Power analysis
- Methods for missing data
- Survey methods & design
- Quasi-experimental methods and causal inference