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Bio
He is co-founder and co-director of the Meta-research innovation Center at Stanford (METRICS), a group dedicated to examining and improving the reproducibility and efficiency of biomedical research. He led the Stanford CTSA KL2 and TL1 training programs from 2012-2019.
Dr. Goodman's own research concerns the proper measurement, conceptualization and synthesis of research evidence, with particular emphasis on Bayesian approaches to quantitation, and qualitative approaches arising from the philosophy of science. He is also interested in developing methods to use shared data to confirm and extend published science, as well as to explore new hypotheses. He also has worked on the connections between ethics and scientific methods, particularly in the domain of interventional research, and policy making. Finally, he has a strong interest in developing curricula and new models for teaching the foundations of good scientific practice, from question development to proper study design, conduct, analysis and inference. He teaches courses on clinical research methods, foundations of scientific and statistical reasoning, and evaluation of diagnostic and predictive technologies.
Dr. Goodman's own research concerns the proper measurement, conceptualization and synthesis of research evidence, with particular emphasis on Bayesian approaches to quantitation, and qualitative approaches arising from the philosophy of science. He is also interested in developing methods to use shared data to confirm and extend published science, as well as to explore new hypotheses. He also has worked on the connections between ethics and scientific methods, particularly in the domain of interventional research, and policy making. Finally, he has a strong interest in developing curricula and new models for teaching the foundations of good scientific practice, from question development to proper study design, conduct, analysis and inference. He teaches courses on clinical research methods, foundations of scientific and statistical reasoning, and evaluation of diagnostic and predictive technologies.
Research Interests
Papers共 45 篇Author StatisticsCo-AuthorSimilar Experts
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NEJM evidenceno. 1 (2023): EVIDoa2300003-EVIDoa2300003
Springer eBookspp.33-100, (2023)
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mag(2014)
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mag(2013)
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