Health Policy Statistics Hub

Evidence-Based Guidelines  ·  Population Health Policy  ·  Public Health Impact

Garnet Anderson develops and applies statistical frameworks to evaluate population-level evidence that informs health policy and clinical guidelines. Her work leverages large, long-running cohorts to assess the long-term effects of prevention strategies, quantify benefits and harms, and support evidence-based decision-making in women’s health and cancer prevention.

Cancer Screening Policy  ·  Policy Modeling  ·  Comparative Effectiveness

Ruth Etzioni develops statistical and simulation models to evaluate cancer screening policies at the population level, with a focus on quantifying benefits, harms, and uncertainty across alternative guideline scenarios. The Etzioni Lab examines overdiagnosis, diagnostic performance, lead time, and mortality trade-offs for prostate, breast, and colorectal cancer screening strategies. She contributes core modeling expertise to national guideline development, including work supporting the U.S. Preventive Services Task Force (USPSTF) helping inform evidence-based screening recommendations.

Risk Prediction  ·  Genetic Epidemiology  ·  Population Health

Li Hsu develops statistical methods to study genetic and environmental contributions to cancer risk at the population level. Her work integrates genomic data with lifestyle and family history information to support risk-based policy analysis and evaluation of alternative screening and prevention strategies. She serves as a lead biostatistician for large national consortia, including the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), and contributes methodological expertise that informs guideline development and population-level risk stratification.

Survivorship Policy·  Long-Term Follow-Up  ·  Population Health Outcomes

Wendy Leisenring develops statistical analyses of long-term health outcomes among childhood cancer survivors to inform survivorship policy and follow-up guidelines. Her work examines chronic conditions, subsequent malignancies, and functional outcomes across the life course, providing population-level evidence to guide screening recommendations and long-term care strategies for survivor populations.

Screening Policy Evaluation  ·  Microsimulation Modeling  ·  Health Services Research

Carolyn Rutter develops and calibrates microsimulation models to evaluate cancer screening policies and inform evidence-based guideline development. Her research focuses on population-level assessment of diagnostic pathways and screening strategies, particularly for colorectal and breast cancer. She applies Bayesian methods, hierarchical modeling, and evidence synthesis to support national policy analyses, including work informing the U.S. Preventive Services Task Force (USPSTF), and to examine variation in quality and access across healthcare systems. healthcare disparities.

Policy-Relevant Evidence  ·  Prevention and Screening Policy ·  Population Health Statistics

Cathy Tangen develops statistical analyses that translate large-scale clinical evidence into population-level guidance for cancer screening and prevention policy. Drawing on data from major national studies, her work evaluates long-term outcomes and benefit–harm trade-offs to support evidence-based guidelines and public health recommendations.

Translational Statistics  ·  Precision Medicine  ·  Immunogenetics

Lue Ping Zhao brings statistical innovation to policy-relevant research in immunogenetics, especially in the context of type 1 diabetes and infectious disease. His work elucidates genetic triggers of disease progression and supports precision intervention strategies. He contributes methods for genotype interpretation and applies recursive and object-oriented modeling techniques to guide preventive care and stratified treatment policies in autoimmune disease and cancer.

Biomarker Evidence Synthesis  ·  Surveillance Modeling  ·  Screening Policy Evaluation

Yingye Zheng develops statistical methods to evaluate cancer screening and biomarker strategies at the population level, with a focus on longitudinal modeling, risk stratification, and surveillance policy. Her work supports assessment of how emerging detection tools perform across screening pathways and over time, informing evidence-based guideline and policy decisions. She serves as a principal investigator or core methodologist on national initiatives including the Early Detection Research Network (EDRN), Liquid Biopsy Consortium (LBC), and the Population-based Research to Optimize the Screenign Process (PROSPR) initiative, contributing statistical frameworks that shape how cancer detection strategies are evaluated for public health impact.