Portrait of Stuart G Baker, Sc.D.

Stuart G Baker, Sc.D.

Mathematical Statistician Biometry Research Group
Email: bakers@mail.nih.gov Phone: 240-276-7147 Fax: 240-276-7845 Room: 5E606

View publications by Stuart G Baker


Stuart Baker, Sc.D., is a mathematical statistician in the Biometry Research Group. Dr. Baker provides statistical reviews of Division of Cancer Prevention protocols and concepts and is a consultant to scientific decision-makers. Related to biomarker studies, Dr. Baker is interested in the evaluation of prognostic markers for risk prediction, early detection markers for treatment selection, predictive markers, surrogate endpoints, and high-dimensional data. Related to the design and analysis of randomized trials and observational studies, Dr. Baker has expertise in causal inference, missing-data adjustment, categorical data analysis, decision analysis, and graphical methods. Additional areas of expertise include cancer screening evaluation and twin studies. To implement statistical analyses, Dr. Baker applies his expertise in writing software using Mathematica. Dr. Baker is also interested in the puzzling phenomena in early-stage carcinogenesis, especially the detached pericyte hypothesis as an explanation.

Dr. Baker received his Sc.D. in biostatistics from the Harvard School of Public Health and was the first recipient of the Distinguished Alumni Award from the Department of Biostatistics. Dr. Baker is a fellow of the American Statistical Association. He joined the Biometry Research Group in 1985.

Mathematica Packages

  1. Comparative Evaluation of Two Serial Gene Expression Experiments
  2. Composite Linear Models
  3. Estimating the Overdiagnosis Fraction in Cancer Screening
  4. Evaluating Risk Prediction Markers via Relative Utility Curves
  5. Evaluating Predictive Markers in a Randomized Trial with Binary Outcomes
  6. Evaluating Predictive Markers in a Randomized Trial with Survival Outcomes
  7. The Latent Class Twin Method
  8. Predicting Treatment Effect from Surrogate Endpoints and Historical Trials
  9. Simple and Flexible Classification of Gene Expression Microarrays Via Swirls and Ripples
  10. The Paired Availability Design and Related Instrumental Variable Meta-analyses