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RETRA’s scientists apply advanced statistical and data analysis methods to evaluate environmental and health-related data, supporting exposure reconstruction, trend interpretation, and evidence-based conclusions in both regulatory and litigation contexts

Consulting Expertise

» Epidemiologic Data Evaluation and Modeling
» Survival Analysis and Hazard Modeling (Cox PH, Simulation Studies)
» Statistical Review of Cancer Registry and Health Outcomes Data
» Machine Learning Applications in Health and Environmental Datasets
» Exposure Reconstruction and Uncertainty Analysis
» Historical Data and Background Level Comparisons
» Geospatial and Environmental Data Analysis
» Critical Review of Epidemiologic Literature and Study Design

Selected Project Experience

Simulation of Epidemiologic Methods: Developed population-representative survival datasets to evaluate the effects of confounding and model assumptions on epidemiologic mortality findings. Used these simulations to test the robustness of Cox Proportional Hazards modeling under conditions of assumption violation.

Cancer Registry and Workplace Analysis: Analyzed cancer surveillance data to assess relevance of breast cancer rates among employees at a workplace where excess cases were suspected. Applied statistical methods to determine whether observed incidence exceeded expected background levels.

National-Scale Epidemiologic Studies: Co-authored analyses of U.S. and Canadian cohort studies examining associations between air pollution exposures (PM2.5, ozone, traffic-related pollutants) and health outcomes including ischemic heart disease, diabetes, hypertension, adverse birth outcomes, and mortality. Contributed statistical and geospatial modeling expertise to predict pollution exposures.

Medicaid Cost Analysis: Applied machine learning techniques to large health claims datasets to identify key drivers of Medicaid expenditures, improving understanding of healthcare utilization and economic impacts.

Exposure Modeling in Industrial and Environmental Scenarios: Created models to characterize potential exposures from industrial incidents, such as a high-volume waste stream rupture in a silicon chip manufacturing facility, to inform evacuation and PPE guidance.