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DTSTART;TZID=America/New_York:20220301T130000
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SUMMARY:Frontiers in Biostatistics: Considerations for Extracting Real-World Evidence from Real-World Data
DESCRIPTION:Tuesday\, March 1\, 2022\n1:00pm Eastern Time \nRebecca A. Hubbard\, PhD\nProfessor of Biostatistics\nUniversity of Pennsylvania Perlman School of Medicine \nYouTube Video \nAbstract: Opportunities to use real-world data (RWD)\, including electronic health records (EHR) and medical claims data\, have exploded over the past decade. The Covid-19 pandemic has provided a particularly dramatic illustration of the potential value of RWD for advancing medical research as well as the high-risk of bias inherent to many such studies. Using data sources that were not collected for research purposes comes at a cost\, and naïve use of these data without considering their complexity and imperfect quality can lead to biased inference. While RWD offer the opportunity to generate timely evidence grounded in real-world populations and clinical practice\, issues of information bias and confounding create serious threats to the validity of these studies. The statistician is faced with a quandary: how to effectively utilize RWD to advance research without compromising best practices for principled data analysis. In this talk I will use examples from my research on methods for the analysis of EHR derived-data to illustrate how an understanding of the EHR data generating mechanism can inform selection of appropriate study questions and development and application of statistical methods that minimize the risk of bias. The overarching goal of this presentation is to raise awareness of challenges associated with the analysis of RWD and demonstrate that valid evidence generation can be grounded in an understanding of the scientific context and data generating process. \n  \nDr. Hubbard’s research focuses on the development and application of methods to improve analyses using real world data sources including electronic health records (EHR) and claims data. The data science era demands novel analytic methods to transform the wealth of data created as a byproduct of our digital interactions into valid and generalizable knowledge. Dr. Hubbard’s research emphasizes statistical methods designed to meet this challenge by addressing the messiness and complexity of real world data including informative observation schemes\, phenotyping error\, and error and missingness in confounders. Her methods have been applied to support the advancement of a broad range of research areas through use of EHR and claims data including health services research\, cancer epidemiology\, aging and dementia\, and pharmacoepidemiology. \n 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-using-ehr-to-accelerate-cancer-outcomes-research-without-leaving-valid-inference-behind/
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20220304T120000
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LAST-MODIFIED:20220304T195647Z
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SUMMARY:Data Science Seminar: Radiomics for Feature Extraction from Radiological Images
DESCRIPTION:Friday\, March 4\, 2022\n12:00PM Eastern Time \nAni Eloyan\, PhD\nAssistant Professor\nDepartment of Biostatistics\, Brown University \nRegister. \nAbstract: Cancer patients routinely undergo radiological evaluations where images of various modalities including computed tomography\, positron emission tomography\, and magnetic resonance images are collected for diagnosis and for evaluation of disease progression. Tumor characteristics\, often referred to as measures of tumor heterogeneity\, can be computed using these clinical images and used as predictors of disease progression and patient survival. Several approaches to quantifying tumor heterogeneity have been proposed including simple intensity histogram-based measures\, metrics attempting to quantify average distance from a homogeneous surface\, and texture analysis-based methods. I will present a statistical framework for principal manifold estimation for obtaining a smooth estimate of tumor surface. I will describe an estimation procedure for tumor heterogeneity using clustering methods considering the neighborhood topology of the tumors. The proposed approach incorporates the spatial structure of the tumor image using neighborhood summary measures. The results of using the proposed methods to estimate cancer survival using machine learning algorithms will be discussed.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-radiomics-for-feature-extraction-from-radiological-images/
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20220307T130000
DTEND;TZID=America/New_York:20220307T140000
DTSTAMP:20260421T235202
CREATED:20220301T215910Z
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SUMMARY:Data Science Seminar: Engineering Protease Activity Sensors For Personalized Detection and Profiling of Cancer
DESCRIPTION:Monday March 7th\, 2022\n1:00PM Eastern Time \nAva Soleimany\, PhD\nSenior Researcher\, Biomedical Machine Learning Group at Microsoft Research\, New England \nAbstract: Precision cancer medicine envisions a world where diagnostic and therapeutic opportunities are intelligently tailored to individual patient needs. Achieving this vision necessitates access to high quality\, accurate\, and individualized information about disease state. Engineered probes that sense disease activity — dynamically and directly within the body — could provide this information by generating signals that functionally measure the state of one’s disease. \nIn this talk\, I will discuss my work in engineering a novel class of nanoscale sensors that directly query tumor microenvironments by measuring the activity of proteases\, enzymes directly involved in all functional hallmarks of cancer. I will share how we can leverage the rich\, functional data generated by these sensors to design and deploy novel\, expressive\, and high-fidelity machine learning models to power individualized diagnostic decision-making and noninvasive monitoring. Finally\, I show how we can engineer sensors to spatially profile protease activity in situ\, demonstrating their ability to discover personalized insights into mechanisms of tumor progression and treatment response and encouraging their translation for precision cancer medicine.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-engineering-protease-activity-sensors-for-personalized-detection-and-profiling-of-cancer/
CATEGORIES:Seminar
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