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DTSTART;TZID=America/New_York:20260423T160000
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SUMMARY:Evidence Triangulation in Dementia Research
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday April 23 at 4:00pm\nHSPH\, FXB 301 \nMaria Glymour\, ScD\, Chair and Professor of Epidemiology\, Boston University School of Public Health \nResearch on cognitive aging\, including development of neurodegenerative diseases such as Alzheimer’s\, is fraught with causal inference challenges. This talk will briefly review why identifying the causes and potential prevention strategies for dementia is particularly challenging. I will discuss a framework for evidence triangulation and offer examples of promising approaches in dementia research.
URL:https://ds.dfci.harvard.edu/event/evidence-triangulation-in-dementia-research/
CATEGORIES:Seminar
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SUMMARY:Building Data Analysis Proofs
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday April 30 at 4:00pm\nHSPH\, FXB 301 \nRoger Peng\, PhD\, Professor of Statistics and Data Sciences\, University of Texas at Austin \nData analyses are often constructed in an imperative manner\, where commands representing actions taken on the data are issued sequentially. The publication of these commands\, along with the data\, is essential to the reproducibility of the analysis by others. However\, simply presenting the code and the results of running the code can hide important details about the data analyst’s premises\, expectations\, and assumptions about the data. Understanding this analysis reasoning can be critical to evaluating the quality of an analysis and for suggesting possible improvements. We argue that a formal representation of a data analysis that externalizes its logical construction offers more useful information for statically illustrating an analyst’s reasoning. Such a formal representation would allow for the evaluation of some aspects of a data analysis without the need for the data\, the visualization of the logical connections leading to a conclusion\, and the ability to assess the sensitivity of an analyst’s assumptions to unexpected features in the data. In this talk I will describe an implementation of this formal representation and how it might be applied to some common data analysis tasks. \n\nColloquium Seminar Series
URL:https://ds.dfci.harvard.edu/event/building-data-analysis-proofs/
CATEGORIES:Seminar
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