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SUMMARY:COVID-19 Data Science Zoomposium
DESCRIPTION:Caroline Buckee\, Department of Biostatistics\nHarvard TH Chan School of Public Health\nHow do we predict the pandemic? \nMichael Mina\, Department of Epidemiology\nHarvard TH Chan School of Public Health\nThe importance and challenges of testing for COVID-19 \nNatalie Dean\, Department of Biostatistics\, University of Florida\nHow we evaluate the efficacy of potential therapies and vaccines  \nAlexis Madrigal\, The Atlantic\nJournalism in the time of COVID-19 \nModerated by Rafael Irizarry \nSponsored by the Department of Data Sciences\, Dana-Farber Cancer Institute\nand the Brown Institute for Media Innovation\, Columbia University
URL:https://ds.dfci.harvard.edu/event/covid-19-data-science-zoomposium/
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20200428T130000
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SUMMARY:Reframing proportional-hazards modeling for large time-to-event datasets with applications to deep learning
DESCRIPTION:Frontiers in Biostatistics Seminar\nNoah Simon\, Ph.D.\nAssociate Professor\nDepartment of Biostatistics\nUniversity of Washington\n\nTo build inferential or predictive survival models\, it is common to assume proportionality of hazards and fit a model by maximizing the partial likelihood. This has been combined with non-parametric and high dimensional techniques\, eg. spline expansions and penalties\, to flexibly build survival models. \nNew challenges require extension and modification of that approach. In a number of modern applications there is interest in using complex features such as images to predict survival. In these cases\, it is necessary to connect more modern backends to the partial likelihood (such as deep learning infrastructures based on eg. convolutional/recurrent neural networks). In such scenarios\, large numbers of observations are needed to train the model. However\, in cases where those observations are available\, the structure of the partial likelihood makes optimization difficult (if not completely intractable). \nIn this talk we show how the partial likelihood can be simply modified to easily deal with large amounts of data. In particular\, with this modification\, stochastic gradient-based methods\, commonly applied in deep learning\, are simple to employ. This simplicity holds even in the presence of left truncation/right censoring\, and time-varying covariates. This can also be applied relatively simply with data stored in a distributed manner.
URL:https://ds.dfci.harvard.edu/event/reframing-proportional-hazards-modeling-for-large-time-to-event-datasets-with-applications-to-deep-learning/
CATEGORIES:Seminar
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