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DTSTART;TZID=America/New_York:20201013T130000
DTEND;TZID=America/New_York:20201013T140000
DTSTAMP:20260412T124112
CREATED:20200430T165238Z
LAST-MODIFIED:20201109T234833Z
UID:894-1602594000-1602597600@ds.dfci.harvard.edu
SUMMARY:Constructing Confidence Interval for RMST under Group Sequential Setting
DESCRIPTION:Frontiers in Biostatistics Seminar\nOctober 13\, 2020\n1:00PM \nLu Tian\, PhD\nAssociate Professor of Biomedical Data Science in the School of Medicine\nStanford University \nIt is appealing to compared survival distributions based on restricted mean survival time (RMST)\, since it generates a clinically interpretable summary of the treatment effect\, which can be estimated nonparametrically without assuming restrictive model assumptions such as the proportional hazards assumption. However\, there are special challenges in designing and analyzing group sequential study based on RMST\, because the truncation timepoint of the RMST in the interim analysis often differs from that in the final analysis. A valid test controls the unconditional type one error has been developed in the past. However\, there is no appropriate statistical procedure for constructing the confidence interval for the treatment effect measured by a contrast in RMST\, while it is crucial for informative clinical decision making. In this talk\, I will review some important design issues for study based on RMST. I will then discuss how to conduct hypothesis testing and how to construct confidence intervals for the difference RMST in a group sequential setting. Examples and numerical studies will be presented to illustrate the method. \nA recording of this seminar is available on your YouTube Channel.
URL:https://ds.dfci.harvard.edu/event/constructing-confidence-interval-for-rmst-under-group-sequential-setting/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/lutian.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200930T130000
DTEND;TZID=America/New_York:20200930T163000
DTSTAMP:20260412T124112
CREATED:20200429T164355Z
LAST-MODIFIED:20201102T114527Z
UID:659-1601470800-1601483400@ds.dfci.harvard.edu
SUMMARY:Marvin Zelen Symposium 2020
DESCRIPTION:In December of 2019\, we planned the next Marvin Zelen Symposium for March of 2020. At that point\, the year ahead seemed to be a data spectacle waiting to happen. Think about all the events planned for 2020! Here’s what we wrote. \nThe topic of this year’s Marvin Zelen Symposium has to do with the year itself — 2020. This first year of a new decade brings with it statistical spectacles on a massive scale. From the Decennial Census and the Presidential Election in the US\, to the 50th anniversary of Earth Day\, to the Summer Olympics in Tokyo. These big events are bristling with data and its analyses — shaping how they are experienced\, discussed\, debated\, and even litigated. Their time scales range from fractions of a second (the difference between gold and silver) to millions of years (the length of the geological record needed to understand our changing climate). They can be influenced by the actions of one person or the cumulative behaviors of hundreds of millions (as voters\, as competitors\, as spectators\, as consumers). The Marvin Zelen Symposium will look at the major data spectacles of 2020\, each one having synergy with the others\, but also adding something new in our understanding of how data establish and reinforce systems of power on our planet. \nWell\, since we organized the 2020 Symposium\, the entire world has changed. The original Zelen event was replaced with a “Zoomposium” on Covid-19\, attracting nearly 500 attendees\, and we moved the program covering everything from the Olympics to Earth Day to September\, 2020 — this month. \nBut now that the date of the rescheduled event is upon us\, think about what Covid-19 has done to many of the events mentioned in our abstract — we have seen everything from complications to cancellations. Still\, we have decided to go forward with the 2020 Zelen Symposium as we had originally planned it. \nWe expect the difficulties of this year will inflect each of the talks on the program. Marvin Zelen embraced the complexity of data and its relationship to lived experience — the same is true for this edition of the Symposium bearing his name. \nSpeakers: \n\nBen Hansen\, University of Michigan\, “The Test of Heightened Election-Year Scrutiny and the Tests in your Table 1”\n\nBrendan Nyhan\, Dartmouth College\, “Selective Exposure to Misinformation: Understanding the Consumption of False News and Anti-Vaccine Content Online”\nRegina Nuzzo\, Gallaudet University\, “Brain-Hacking Stats Communication: A Quick Tour”\nAmy O’Hara\, Georgetown University\, “The 2020 Census: Expected vs. Actual”\nAndrew Revkin\, Columbia University\, “Taming Social Media for Constructive Climate Crosstalk”\n\nCharles Stewart\, Massachusetts Institute of Technology\, “Puzzling Through Demand for Mail Voting in the 2020 Election”\nDaniel Webb\, United States Olympic Committee\, “Forecasting the Olympic Games: Team USA’s Medal Prediction Models”\n\nJeremy White\, New York Times\, “Visualizing the Performances of the World’s Greatest Athletes”\n\n\nRegister by September 29\, 2020. \nAudience is capped at 500 participants. Participate on Twitter: #Zelen2020.
URL:https://ds.dfci.harvard.edu/event/marvin-zelen-symposium-2020/
CATEGORIES:Conference
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/logo_fall_narrow_small.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200929T130000
DTEND;TZID=America/New_York:20200929T140000
DTSTAMP:20260412T124112
CREATED:20200922T173720Z
LAST-MODIFIED:20200923T183848Z
UID:2146-1601384400-1601388000@ds.dfci.harvard.edu
SUMMARY:3D Spatial Organization Within Tumors
DESCRIPTION:Data Science Seminar \nSeptember 29\, 2020\n1:00PM ET \nMartin Aryee\, PhD\nAssistant Professor of Pathology\, Harvard Medical School\nAssistant Molecular Pathologist\, Massachusetts General Hospital \nZoom link: https://dfci.zoom.us/j/95524743149?pwd=SzN4cjJZUnhsNVl3dXNmZjZ1N3F4QT09 \nAbstract: The spatial organization of biological systems can impart additional functionality beyond that of the individual components. This is true at a range of scales – from cells in a tissue to individual genes and regulatory elements in a single genome. High-throughput assays that permit spatial measurements have advanced greatly in the past decade and revealed oncogenic architectural alterations in tumors at the tissue\, cellular and chromosome levels. Here I will discuss tumor spatial organization in gastrointestinal malignancies at two different scales. First\, I will describe how we used single-cell RNA-Seq and RNA in situ hybridization in pancreatic cancer to identify cell state and tissue architecture changes induced by contact with stromal cancer-associated fibroblasts (CAFs). We analyzed the spatial architecture of cell states in 195 pancreatic tumors\, mapping over 300\,000 individual cancer cells. We were able to classify different types of tumor glands based on their cell-type composition\, and show that these functional units can be used to characterize tumors in ways not evident from analyses of dissociated single cells. Second\, I will discuss findings from a recent study of 3D genome organization within colon cancer cell nuclei. We found oncogenic changes at the level of regulatory chromatin loops and topologically associated domains (TADs)\, but the most surprising and striking change involves a large-scale repackaging of heterochromatin that appears to restrain tumor progression\, representing a failed anti-tumor epigenetic brake.
URL:https://ds.dfci.harvard.edu/event/3d-spatial-organization-within-tumors/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/aryee.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200922T140000
DTEND;TZID=America/New_York:20200922T150000
DTSTAMP:20260412T124112
CREATED:20200915T212158Z
LAST-MODIFIED:20200917T142852Z
UID:2101-1600783200-1600786800@ds.dfci.harvard.edu
SUMMARY:Cancer Development\, Heterogeneity and Dynamics from Premalignancy to Drug Refractory Disease
DESCRIPTION:Data Science Seminar \nSeptember 22\, 2020\n2:00PM ET \nIgnaty Leshchiner\, PhD\nPostdoctoral Fellow\, Harvard Medical School/Brigham and Women’s Hospital \nZoom link: http://bit.ly/DSSept22 \nAbstract: \nReal-time study of tumor emergence and progression in patients will help predict and ultimately change the course of the patient’s disease. This could be achieved by inferring genotypes of heterogeneous cell populations within the tumor\, their fitness\, growth rates\, corresponding expression patterns and drug tolerance states. We have developed a set of computational methods to infer the order of tumor-initiating events and to follow the dynamics and competition of cancer cell populations during disease progression and treatment. The package\, PhylogicNDT\, uses tumor genomic data to reconstruct the process of tumor formation\, natural growth kinetics\, competition and spread of resistance clones. We applied this package to 2\,658 primary cancers to reconstruct developmental trajectories and history of common tumor types in premalignancy and early malignancy state; reconstruct cancer cell populations and growth rates\, fitness and kinetics of individual clones during natural progression of leukemia in vivo; analyze spatial progression of resistance clones and find new resistance mechanisms in a large cohort of rapid autopsy cases. By integrating blood biopsy (ctDNA)\, solid tissue biopsy and autopsy data we show that resistance often emerges in multiple distant metastatic sites simultaneously\, with evidence of multiple resistance mutations present in the blood’s ctDNA at the same time. Finally\, we combine bulk and single cell sequencing data to help identify genetically distinct clones and explain their phenotypic differences. We envision that treatment decisions will improve with better understanding of tumor development\, clonal structure and microenvironment\, and the path tumor takes to become malignant and progress after treatment.
URL:https://ds.dfci.harvard.edu/event/cancer-development-heterogeneity-and-dynamics-from-premalignancy-to-drug-refractory-disease/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/10221_Facebook_360x360.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200915T130000
DTEND;TZID=America/New_York:20200915T140000
DTSTAMP:20260412T124112
CREATED:20200708T162715Z
LAST-MODIFIED:20201109T234809Z
UID:1804-1600174800-1600178400@ds.dfci.harvard.edu
SUMMARY:A New Hybrid Phase I-II-III Clinical Trial Paradigm
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday September 15\, 2020 at 1:00PM Eastern Time \nPeter F. Thall\, PhD\nDepartment of Biostatistics\nUniversity of Texas M.D. Anderson Cancer Center \nAbstract: Conventional evaluation of a new drug\, 𝐴\, is done in three phases. Phase I relies on toxicity to determine a “maximum tolerable dose” (MTD) of 𝐴\, in phase II it is decided whether 𝐴 at the MTD is “promising” in terms of response probability\, and if so a large randomized phase III trial is conducted to compare 𝐴 to a control treatment\, 𝐶\, based on survival time or progression free survival time. This paradigm has many flaws. The first two phases may be combined by conducting a phase I-II trial\, which chooses an optimal dose based on both efficacy and toxicity\, with evaluation of 𝐴 at the optimal phase I-II dose then done in phase III. In this talk\, I will describe a new paradigm\, motivated by the possibility that the optimal phase I-II dose may not maximize mean survival time with 𝐴. A hybrid phase I-II-III design is presented that allows the optimal phase I-II dose of 𝐴 to be re-optimized based on survival time data after the first stage of phase III. The hybrid design relies on a mixture model for the survival time distribution as a function of efficacy\, toxicity\, and dose. A simulation study is presented to evaluate the design’s properties\, including comparison to the more conventional approach that does not re-optimize the dose of 𝐴 in phase III. \nA recording of this seminar is available on your YouTube Channel.
URL:https://ds.dfci.harvard.edu/event/a-new-hybrid-phase-i-ii-iii-clinical-trial-paradigm/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/07/thall_peter.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200902T130000
DTEND;TZID=America/New_York:20200902T140000
DTSTAMP:20260412T124112
CREATED:20200811T135007Z
LAST-MODIFIED:20201109T234620Z
UID:1913-1599051600-1599055200@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Data Visualization Literacy: Avoiding common mistakes and recognizing deceitful practices
DESCRIPTION:Wednesday September 2\, 2020\n1:00PM ET \nA conversation with Alberto Cairo\nKnight Chair in Visual Journalism at the School of Communication of the University of Miami\nModerator: Rafael Irizarry \nA recording of the talk is available on our YouTube channel. \n  \n 
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-data-visualizacion-literacy-avoiding-common-mistakes-and-recognizing-deceitful-practices/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/08/Alberto_Cairo-02.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200826T100000
DTEND;TZID=America/New_York:20200826T120000
DTSTAMP:20260412T124112
CREATED:20200811T132449Z
LAST-MODIFIED:20200811T132513Z
UID:1899-1598436000-1598443200@ds.dfci.harvard.edu
SUMMARY:Training Sessions: InForm
DESCRIPTION:Wednesday August 26 from 10:00am to 12:00PM \nDanny Quinn will demonstrate extracting DF/HCC clinical trials data from InForm (which is the electronic data capture system (eDC)) into SAS data sets. There will also be a discussion on the attributes of the data sets and various utilities which support the extraction process.
URL:https://ds.dfci.harvard.edu/event/training-sessions-inform/
CATEGORIES:Training Session
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/08/laptop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200811T130000
DTEND;TZID=America/New_York:20200811T140000
DTSTAMP:20260412T124112
CREATED:20200716T192733Z
LAST-MODIFIED:20201109T234605Z
UID:1841-1597150800-1597154400@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Increasing Diversity in Data Science
DESCRIPTION:A conversation with Emma Benn\, DrPh\nAssociate Professor\, Center for Biostatistics and Department of Population Health Science and Policy\nIcahn School of Medicine at Mount Sinai \nModerator: Rafael Irizarry \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-increasing-diversity-in-data-science/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/07/Feb18_EmmaBenn.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200804T130000
DTEND;TZID=America/New_York:20200804T140000
DTSTAMP:20260412T124112
CREATED:20200716T191709Z
LAST-MODIFIED:20201109T234542Z
UID:1835-1596546000-1596549600@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Data-driven Movement Monitoring During the Pandemic
DESCRIPTION:A conversation with Caroline Buckee\, Associate Professor of Epidemiology\, Harvard TH Chan School of Public Health\nModerator: Rafael Irizarry \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-data-driven-movement-monitoring-during-the-pandemic/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/07/caroline-buckee-988a0868ef919cbfdc6e80eed4b52cca.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200721T130000
DTEND;TZID=America/New_York:20200721T140000
DTSTAMP:20260412T124112
CREATED:20200611T150020Z
LAST-MODIFIED:20201109T234455Z
UID:1577-1595336400-1595340000@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: A Comparison of COVID-19 Prediction Models
DESCRIPTION:A conversation with Nicholas Reich\, Associate Professor of Biostatistics\,\nUniversity of Massachusetts Amherst \nModerator: Rafael Irizarry\nhttps://bit.ly/DSJuly21 \nWe are pleased to announce a new weekly Zoominar for Data Science. Rather than a traditional seminar format\, Rafael Irizarry will moderate a Q&A with invited speakers on various topics in data science. Join us for these interactive conversations Tuesdays at 1:00pm EST. Registration is quick and easy. \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-covid-19-prediction-models/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/06/Nicholas_Reich_crop.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200714T130000
DTEND;TZID=America/New_York:20200714T140000
DTSTAMP:20260412T124112
CREATED:20200617T105710Z
LAST-MODIFIED:20201109T234520Z
UID:1707-1594731600-1594735200@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: The Latest on COVID-19 Testing: How Can Testing Help Us Reopen?
DESCRIPTION:A conversation with Michael Mina\, Assistant Professor\nCenter for Communicable Disease Dynamics\, Department of Epidemiology\, Harvard T.H. Chan School of Public Health \nModerator: Rafael Irizarry \nWe are pleased to announce a new weekly Zoominar for Data Science. Rather than a traditional seminar format\, Rafael Irizarry will moderate a Q&A with invited speakers on various topics in data science. Join us for these interactive conversations Tuesdays at 1:00pm EST. Registration is quick and easy. \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-update-on-covid-19-testing-how-do-we-reopen/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/06/Mina-Headshot-1-300x300-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200707T130000
DTEND;TZID=America/New_York:20200707T140000
DTSTAMP:20260412T124112
CREATED:20200610T162527Z
LAST-MODIFIED:20201109T234436Z
UID:1530-1594126800-1594130400@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: A Debate About the Severity of the COVID-19 Pandemic
DESCRIPTION:A conversation with John P.A. Ioannidis\, C. F. Rehnborg Professor In Disease Prevention In The School Of Medicine\, Professor Of Medicine\, Of Health Research And Policy (Epidemiology) And By Courtesy\, Of Statistics And Of Biomedical Data Science\, Stanford University \nModerator: Rafael Irizarry \nWe are pleased to announce a new weekly Zoominar for Data Science. Rather than a traditional seminar format\, Rafael Irizarry will moderate a Q&A with invited speakers on various topics in data science. Join us for these interactive conversations Tuesdays at 1:00pm EST. Registration is quick and easy. \nhttps://bit.ly/DSJuly7 \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-data-driven-versus-non-data-driven-decision-making-during-the-covid-pandemic/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/06/john-ioannidis_profilephoto.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200630T130000
DTEND;TZID=America/New_York:20200630T140000
DTSTAMP:20260412T124112
CREATED:20200610T162320Z
LAST-MODIFIED:20201109T234416Z
UID:1527-1593522000-1593525600@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Massachusetts Data from the COVID-19 Response
DESCRIPTION:A conversation with Gillian Haney\, Director of Infectious Disease Surveillance and Informatics\, Massachusetts Department of Public Health and Catherine (Katie) Brown\, State Public Health Veterinarian\, Massachusetts Department of Public Health \nModerator: Rafael Irizarry \nWe are pleased to announce a new weekly Zoominar for Data Science. Rather than a traditional seminar format\, Rafael Irizarry will moderate a Q&A with invited speakers on various topics in data science. Join us for these interactive conversations Tuesdays at 1:00pm EST. Registration is quick and easy. \nhttps://bit.ly/DSJune30 \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-massachusetts-data-from-the-covid-19-response/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/06/mdph.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200623T130000
DTEND;TZID=America/New_York:20200623T140000
DTSTAMP:20260412T124112
CREATED:20200610T161930Z
LAST-MODIFIED:20201109T234351Z
UID:1522-1592917200-1592920800@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications
DESCRIPTION:A conversation with Elisabeth Bik\, PhD\, Microbiome & Science Integrity Consultant\, Harbers Bik LLC \nModerator: Rafael Irizarry\, PhD \nWe are pleased to announce a new weekly Zoominar for Data Science. Rather than a traditional seminar format\, Rafael Irizarry will moderate a Q&A with invited speakers on various topics in data science. Join us for these interactive conversations Tuesdays at 1:00pm EST. Registration is quick and easy. \nhttps://bit.ly/DSJune23 \nA recording of the talk is available on our YouTube channel. \n 
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-the-prevalence-of-inappropriate-image-duplication-in-biomedical-research-publications/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/06/bik_crop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200609T130000
DTEND;TZID=America/New_York:20200609T140000
DTSTAMP:20260412T124112
CREATED:20200525T115617Z
LAST-MODIFIED:20201109T234334Z
UID:1484-1591707600-1591711200@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: The Role of Statistical Learning in Applied Statistics
DESCRIPTION:A conversation with\nDaniela Witten\, PhD\nUniversity of Washington \nModerator: Rafael Irizarry \nRegistration required:\nhttps://dfci.zoom.us/webinar/register/WN_YvsckwdGSxmTT-GeyLGOOA \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-the-role-of-statistical-learning-in-applied-statistics/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/05/daniela-crop.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200604T130000
DTEND;TZID=America/New_York:20200604T140000
DTSTAMP:20260412T124112
CREATED:20200525T115308Z
LAST-MODIFIED:20201109T234313Z
UID:1479-1591275600-1591279200@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: COVID-19 Update
DESCRIPTION:A conversation with\nMarc Lipsitch\, PhD\nHarvard TH Chan School of Public Health \nModerator: Rafael Irizarry \nRegistration required:\nhttps://dfci.zoom.us/webinar/register/WN_F_ROPV-RRNyXGoXaoTWFNw \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-covid-19-update/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/05/lipsitch_crop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200602T130000
DTEND;TZID=America/New_York:20200602T140000
DTSTAMP:20260412T124112
CREATED:20200525T114906Z
LAST-MODIFIED:20201109T234246Z
UID:1472-1591102800-1591106400@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Update on COVID-Related Trials
DESCRIPTION:A conversation with\nNatalie Dean\, PhD\nUniversity of Florida \nModerator: Rafael Irizarry \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-update-on-covid-related-trials/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/05/Natalie-Dean_crop_sm.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200526T130000
DTEND;TZID=America/New_York:20200526T140000
DTSTAMP:20260412T124112
CREATED:20200525T114005Z
LAST-MODIFIED:20201109T234232Z
UID:1459-1590498000-1590501600@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Teaching Data Science to the Masses
DESCRIPTION:A conversation with Jeff Leek\, PhD\, Johns Hopkins University. \nModerator: Rafael Irizarry. \nRegistration required. \nhttps://dfci.zoom.us/webinar/register/WN_XscX-d21RqylhDCXzvP2-Q \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-teaching-data-science-to-the-masses/
CATEGORIES:Seminar,Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/05/leek-crop-e1590407126153.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200519T130000
DTEND;TZID=America/New_York:20200519T140000
DTSTAMP:20260412T124112
CREATED:20200429T173724Z
LAST-MODIFIED:20200915T200724Z
UID:667-1589893200-1589896800@ds.dfci.harvard.edu
SUMMARY:Streamlined empirical Bayes estimation for contextual bandits with applications in mobile health
DESCRIPTION:Frontiers in Biostatistics Webinar\nMarianne Menictas\nPostdoctoral Fellow\, Department of Statistics\nHarvard University \nMobile health (mHealth) technologies are increasingly being employed to deliver interventions to users in their natural environments. With the advent of increasingly sophisticated sensing devices (e.g.\, GPS) and phone-based EMA\, it is becoming possible to deliver interventions at moments when they can most readily influence a person’s behavior. For example\, for someone trying to increase physical activity\, moments when the person can be active are critical decision points when a well-timed intervention could make a difference. The promise of mHealth hinges on the ability to provide interventions at times when users need the support and are receptive to it. Thus\, our goal is to learn the optimal time and intervention for a given user and context. A significant challenge to learning is that there are often only a few opportunities per day to provide treatment. Additionally\, when there is limited time to engage users\, a slow learning rate can pose problems\, potentially raising the risk that users will abandon the intervention. To prevent disengagement\, a learning algorithm should learn quickly in spite of noisy measurements. To accelerate learning\, information may be pooled across users and time in a dynamic manner\, combining a contextual bandit algorithm with a Bayesian random effects model for the reward function. As information accumulates\, however\, tuning user and time specific hyperparameters becomes computationally intractable. In this talk\, we focus on solving this computational bottleneck.
URL:https://ds.dfci.harvard.edu/event/streamlined-hyper-parameter-tuning-in-mobile-health/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200428T130000
DTEND;TZID=America/New_York:20200428T140000
DTSTAMP:20260412T124112
CREATED:20200428T180127Z
LAST-MODIFIED:20220525T113511Z
UID:520-1588078800-1588082400@ds.dfci.harvard.edu
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200402T130000
DTEND;TZID=America/New_York:20200402T140000
DTSTAMP:20260412T124112
CREATED:20200612T115313Z
LAST-MODIFIED:20200612T122256Z
UID:1618-1585832400-1585836000@ds.dfci.harvard.edu
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
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/06/logo.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200226T100000
DTEND;TZID=America/New_York:20200226T120000
DTSTAMP:20260412T124112
CREATED:20200429T162749Z
LAST-MODIFIED:20200511T121722Z
UID:651-1582711200-1582718400@ds.dfci.harvard.edu
SUMMARY:Introduction to single cell RNA-seq data analysis for statisticians
DESCRIPTION:Data Science Training Session\nKelly Street\, Research Fellow\nEtai Jacob\, Research Fellow \nIn this short course\, we will introduce some of the most widely used tools for single cell analysis. We will describe common experimental methods utilized in the community to generate single cell RNA-seq data and demonstrate modern pre-processing and analysis pipelines. In addition\, we will discuss potential problems which frequently show up in analysis and ways to deal with them. After our first meeting\, participants will be encouraged to practice some of the workflows we will share with them in order to discuss issues they encountered during the second meeting.
URL:https://ds.dfci.harvard.edu/event/introduction-to-single-cell-rna-seq-data-analysis-for-statisticians/2020-02-26/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200219T100000
DTEND;TZID=America/New_York:20200219T120000
DTSTAMP:20260412T124112
CREATED:20200429T161423Z
LAST-MODIFIED:20200511T121722Z
UID:644-1582106400-1582113600@ds.dfci.harvard.edu
SUMMARY:Alternatives to Hazard Ratio for Quantifying Treatment Effect on Time-to-Event Outcomes
DESCRIPTION:Data Science Training Session\nHajime Uno\nAssistant Professor\, Department of Data Science\nDana-Farber Cancer Institute
URL:https://ds.dfci.harvard.edu/event/alternatives-to-hazard-ratio-for-quantifying-treatment-effect-on-time-to-event-outcomes/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200207T160000
DTEND;TZID=America/New_York:20200207T170000
DTSTAMP:20260412T124112
CREATED:20200429T163321Z
LAST-MODIFIED:20200612T115603Z
UID:656-1581091200-1581094800@ds.dfci.harvard.edu
SUMMARY:The Clinical Impact of Genomics in the Pediatric Oncology
DESCRIPTION:DFCI Genomics Meetup\nKatherine Janeway\, MD\nPediatric Oncology\nDana-Farber Cancer Institute \nPizza is provided. \n 
URL:https://ds.dfci.harvard.edu/event/the-clinical-impact-of-genomics-in-the-pediatric-oncology/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Meetup
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200122T100000
DTEND;TZID=America/New_York:20200122T120000
DTSTAMP:20260412T124112
CREATED:20200429T131206Z
LAST-MODIFIED:20200511T121722Z
UID:632-1579687200-1579694400@ds.dfci.harvard.edu
SUMMARY:Collaborative Grant Writing and Statistical Methods for Grants
DESCRIPTION:Data Sciences Training Session\nRebecca Gelman\, PhD\nAssociate Professor\, Department of Data Sciences\nDana-Farber Cancer Institute
URL:https://ds.dfci.harvard.edu/event/collaborative-grant-writing-and-statistical-methods-for-grants/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/gelman_TS.png
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200121T130000
DTEND;TZID=America/New_York:20200121T140000
DTSTAMP:20260412T124112
CREATED:20200429T130831Z
LAST-MODIFIED:20200511T121722Z
UID:622-1579611600-1579615200@ds.dfci.harvard.edu
SUMMARY:NEJM Statistical Guidelines for Authors: Under the Hood
DESCRIPTION:Data Sciences Training Session\nDavid Harrington\, PhD\nProfessor\, Department of Data Sciences\nDana-Farber Cancer Institute
URL:https://ds.dfci.harvard.edu/event/david-harrington-presents-nejm-statistical-guidelines-for-authors/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/harrington_TS.png
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191204T050000
DTEND;TZID=America/New_York:20191204T070000
DTSTAMP:20260412T124112
CREATED:20200408T213229Z
LAST-MODIFIED:20200511T121722Z
UID:94-1575435600-1575442800@ds.dfci.harvard.edu
SUMMARY:Design of Phase III Studies
DESCRIPTION:Data Sciences Training Session\nDesign of Phase III Studies\, Part II \nRobert Gray\, PhD\nProfessor\, Department of Data Science\nDana-Farber Cancer Institute
URL:https://ds.dfci.harvard.edu/event/robert-gray-phd-leads-training-on-design-of-phase-iii-studies/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
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END:VEVENT
END:VCALENDAR