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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220204T130000
DTEND;TZID=America/New_York:20220204T140000
DTSTAMP:20260413T102219
CREATED:20220202T172751Z
LAST-MODIFIED:20220202T172751Z
UID:3303-1643979600-1643983200@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Deciphering Tissue Microenvironment from Next Generation Sequencing Data
DESCRIPTION:Friday February 4\, 2022\n1:00PM Eastern Time \nRegister. \nJian Hu\nPhD Candidate\, Department of Biostatistics\, Epidemiology and Informatics\nUniversity of Pennsylvania \nABSTRACT: The advent of high-throughput next-generation sequencing (NGS) technologies has transformed our understanding of cell biology and human disease. As NGS has been adopted earliest by the scientific community\, its use has now become widespread\, and the technology has improved rapidly. At present\, it is now common for laboratories to assay genome-wide transcriptomes of thousands of cells in a single scRNA-seq experiment. In addition\, technologies that enable the measurement of new information\, for example\, chromatin accessibility\, protein quantification\, and spatial location\, have been developed. In order to take full advantage of the multi-modality information when analyzing NGS data\, new methods are demanded. This seminar will introduce several machine learning algorithms for NGS data analysis with different aims\, including cell type classification\, spatial domain detection\, and tumor microenvironment annotation. \nParts of the talk are based on the following two papers: \n\nhttps://www.nature.com/articles/s42256-020-00233-7 \n\nhttps://www.nature.com/articles/s41592-021-01255-8 \nKEYWORDS: single cell RNA sequencing (scRNA-seq)\, Spatial transcriptomics (ST)\, tumor microenvironment\, machine learning
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-deciphering-tissue-microenvironment-from-next-generation-sequencing-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/WechatIMG25-1.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220119T130000
DTEND;TZID=America/New_York:20220119T160000
DTSTAMP:20260413T102219
CREATED:20211112T192533Z
LAST-MODIFIED:20220120T121922Z
UID:3164-1642597200-1642608000@ds.dfci.harvard.edu
SUMMARY:2022 DF/HCC Celebration of Early Career Investigators in Cancer Research
DESCRIPTION:January 19 | 2022 | 1-4PM Eastern Time \nThis annual symposium showcases the talent of early career investigators at the Dana-Farber/Harvard Cancer Center who work in population science\, including epidemiology\, biostatistics\, outcomes\, diversity\, and cancer care delivery research\, and early detection. \nThis year\, Ann Partridge\, MD\, MPH will be our keynote speaker. Dr. Partridge is a Professor of Medicine at Harvard Medical School\, and Vice Chair of Medical Oncology at Dana-Farber Cancer Institute\, where she also serves as Director of the Adult Survivorship Program and leads the Program for Young Women with Breast Cancer. \nTalk title: Improving Care and Outcomes for Young Women with Breast Cancer: From Observation to Intervention \nFollowing Dr. Partridge’s talk will be a panel discussion with Tal Sella\, Tamryn Gray\, and Peter Miller\, and then short talks and posters by our early career investigators. \nAgenda and video: https://www.dfhcc.harvard.edu/ecis \n\n\n\n\nThis symposium is scheduled as a virtual event. 
URL:https://ds.dfci.harvard.edu/event/2022-df-hcc-celebration-of-early-career-investigators-in-cancer-research/
CATEGORIES:Conference
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/11/ECIS2022-Abstracts-DS-Website.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220111T130000
DTEND;TZID=America/New_York:20220111T140000
DTSTAMP:20260413T102219
CREATED:20211130T233743Z
LAST-MODIFIED:20220111T182225Z
UID:3204-1641906000-1641909600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Studies on COVID-19 and Cancer using National Real-World VA Data
DESCRIPTION:Nathanael Fillmore is the Associate Director for Machine Learning and Advanced Analytics at the VA Boston Healthcare System’s Cooperative Studies Program Informatics Center. He leads a data science team focused on using machine learning and data science methods\, in combination with the VA’s large clinical\, genomic\, and imaging databases\, to generate knowledge and resources that will improve care for Veterans with cancer. He also holds a faculty position at Harvard Medical School and is site director for the VA/NCI Big Data-Scientist Training Enhancement Program. Previously\, he was a post-doctoral fellow at Dana-Farber Cancer Institute and Harvard School of Public Health under the direction of Giovanni Parmigiani and Nikhil Munshi. \nThis seminar was not recorded. 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-studies-on-covid-19-and-cancer-using-national-real-world-va-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/11/headshot-square-small.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T130000
DTEND;TZID=America/New_York:20211207T140000
DTSTAMP:20260413T102219
CREATED:20210928T184004Z
LAST-MODIFIED:20211209T121847Z
UID:3064-1638882000-1638885600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Cancer on the Way to Mars
DESCRIPTION:Tuesday\, December 7\, 2021\n1:00pm Eastern Time \nGiovanni Parmigiani\, PhD\nProfessor\nHarvard T.H. Chan School of Public Health \nLink to YouTube Video \nCancer on the Way to Mars Links: \nhttps://www.nap.edu/download/26155 \nhttps://www.env.go.jp/en/chemi/rhm/basic-info/index.html \nhttps://www.nasa.gov/feature/goddard/real-martians-how-to-protect-astronauts-from-space-radiation-on-mars \nhttps://www.nasa.gov/sites/default/files/atoms/files/space_radiation_ebook.pdf \nhttps://standards.nasa.gov/standard/nasa/nasa-std-3001-vol-1 \nhttps://spaceradiation.larc.nasa.gov/nasapapers/2020/5008710.pdf
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-cancer-on-the-way-to-mars/
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2021/09/Parmigiani_Giovanni_sq.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211109T130000
DTEND;TZID=America/New_York:20211109T140000
DTSTAMP:20260413T102219
CREATED:20210917T160314Z
LAST-MODIFIED:20211116T141230Z
UID:3016-1636462800-1636466400@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: A Bayesian Phase I/II Trial Design for Immunotherapy
DESCRIPTION:Tuesday\, November 9\, 2021\n1:00pm Eastern Time \nSuyu Liu\, PhD\nAssociate Professor\nDepartment of Biostatistics\nThe University of Texas MD Anderson Cancer Center \nA Bayesian Phase I/II Trial Design for Immunotherapy \nImmunotherapy is an innovative treatment approach that stimulates a patient’s immune system to fight cancer. It demonstrates characteristics distinct from conventional chemotherapy and stands to revolutionize cancer treatment. We propose a Bayesian phase I/II dose-finding design that incorporates the unique features of immunotherapy by simultaneously considering three outcomes: immune response\, toxicity and efficacy. The objective is to identify the biologically optimal dose\, defined as the dose with the highest desirability in the risk-benefit tradeoff. An Emax model is utilized to describe the marginal distribution of the immune response. Conditional on the immune response\, we jointly model toxicity and efficacy using a latent variable approach. Using the accumulating data\, we adaptively randomize patients to experimental doses based on the continuously updated model estimates. A simulation study shows that our proposed design has good operating characteristics in terms of selecting the target dose and allocating patients to the target dose. \nYouTube Link
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-suyu-liu/
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/09/suyu_liu_quare.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211103T130000
DTEND;TZID=America/New_York:20211103T150000
DTSTAMP:20260413T102219
CREATED:20210928T135710Z
LAST-MODIFIED:20211029T115457Z
UID:3043-1635944400-1635951600@ds.dfci.harvard.edu
SUMMARY:Postdoc Recruitment Day
DESCRIPTION:The Dana-Farber Cancer Institute Department of Data Science announces its second annual Postdoc Recruitment Day to be held on Wednesday\, November 3rd from 1-3pm EST. \nIf you are interested in learning more about postdoctoral opportunities at Dana-Farber Cancer Institute and would like to learn about the research our faculty are conducting\, please sign up for this free event. The faculty participating this year are: \n· Sahand Hormoz\, Assistant Professor \n· Rafael Irizarry\, Professor and Department Chair \n· Heng Li\, Assistant Professor \n· Giovanni Parmigiani\, Professor \n· Mehmet Samur\, Senior Research Scientist \n· Nabihah Tayob\, Assistant Professor \nThe faculty will give brief overviews of their current research. You will also hear from current and former postdocs\, and hear about the resources available in the department\, at Dana-Farber and throughout the Boston area. \nSpace is limited and we request that you are actively looking for a postdoctoral position. Please complete this form to request admission. The deadline to apply is October 29th. \nYou can see our open postdoctoral positions on our website: https://ds.dfci.harvard.edu/careers/
URL:https://ds.dfci.harvard.edu/event/postdoc-recruitment-day/
CATEGORIES:Recruitment
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211028T110000
DTEND;TZID=America/New_York:20211028T120000
DTSTAMP:20260413T102219
CREATED:20211021T234121Z
LAST-MODIFIED:20211029T115538Z
UID:3129-1635418800-1635422400@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Causal Inference Methods for Measures of Health Disparities
DESCRIPTION:Thursday\, October 28\, 2021\n11:00am Eastern Time \nTengfei Li\nGeorgetown University \nCausal Inference Methods for Measures of Health Disparities \nThere is increased interest in the evaluation of health disparities between different socioeconomic groups using data from observational studies. However\, in the absence of randomization\, the results and conclusions may be limited to associations rather than causal effects. The causal inference framework allows us to estimate causal measures for such situations. We present inverse probability weighting (IPW) and doubly robust (DR) estimators for the marginal means of the distributions of the potential outcomes for multiple socioeconomic groups using generalized propensity scores. We estimate the variance of the vector of IPW and DR estimators for the marginal means by using an M-estimation approach. The variances of the estimators for causal measures of health disparities are subsequently estimated using the multivariate delta method. In simulation studies\, the new methods provide coverage probabilities that are close to the nominal level when used to construct 95% confidence intervals for the causal measures of health disparities. \nKeywords: health disparities\, inverse probability weighting estimator\, doubly robust estimator\, generalized propensity score\, M-estimation.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-causal-inference-methods-for-measures-of-health-disparities/
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/10/Screen-Shot-2021-10-21-at-3.45.00-PM.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211019T130000
DTEND;TZID=America/New_York:20211019T140000
DTSTAMP:20260413T102219
CREATED:20210928T181448Z
LAST-MODIFIED:20211201T165250Z
UID:3058-1634648400-1634652000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Treatment-free Survival as a Novel Outcome Measure of Immuno-oncology-based Therapy
DESCRIPTION:Tuesday\, October 19\, 2021\n1:00pm Eastern Time \nMeredith Regan\, ScD\nAssociate Professor\nDepartment of Data Science\, Dana-Farber Cancer Institute\,\nHarvard Medical School \nTreatment-free Survival as a Novel Outcome Measure of Immuno-oncology-based Therapy \nYouTube Link
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-meredith-regan/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2021/09/preferred_meredith_regan_square.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210914T130000
DTEND;TZID=America/New_York:20210914T140000
DTSTAMP:20260413T102219
CREATED:20210614T164652Z
LAST-MODIFIED:20211021T134349Z
UID:2797-1631624400-1631628000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: COVID Vaccine Efficacy Trial Designs\, Open Questions and Statistical Challenges
DESCRIPTION:Tuesday September 14\, 2021\n1:00PM Eastern Time \nHolly Janes\, Ph.D.\nProfessor\, Vaccine and Infectious Disease Division\, Fred Hutchinson Cancer Research Center\nProfessor\, Public Health Sciences Division\, Fred Hutchinson Cancer Research Center \nYouTube video now available.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-holly-janes/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/06/holly-janes-sq.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210616T100000
DTEND;TZID=America/New_York:20210616T120000
DTSTAMP:20260413T102219
CREATED:20210608T184016Z
LAST-MODIFIED:20210630T184005Z
UID:2775-1623837600-1623844800@ds.dfci.harvard.edu
SUMMARY:Training Session: Survival Analysis and Competing Risks Data Analysis
DESCRIPTION:June 16\, 23\, and 30\, 2021\n10:00am-12:00PM Eastern Time \nHaesook Kim\, PhD\nPrincipal Research Scientist\nDepartment of Data Science\, Dana-Farber Cancer Institute\nDepartment of Biostatistics\, Harvard T.H. Chan School of Public Health \nRSVP: https://bit.ly/DSTSJune16
URL:https://ds.dfci.harvard.edu/event/training-session-survival-analysis-and-competing-risks-data-analysis/2021-06-16/
CATEGORIES:Training Session
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/06/kim_headshot.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210608T130000
DTEND;TZID=America/New_York:20210608T140000
DTSTAMP:20260413T102219
CREATED:20210108T131835Z
LAST-MODIFIED:20211021T134536Z
UID:2398-1623157200-1623160800@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: The Use of External Control Data for Predictions and Interim Analyses in Clinical Trials
DESCRIPTION:June 8\, 2021\n1:00PM \nLorenzo Trippa\, PhD\nAssociate Professor\nDepartment of Biostatistics\, Harvard T.H. Chan School of Public Health\nDepartment of Data Science\, Dana-Farber Cancer Institute \nThe Use of External Control Data for Predictions and Interim Analyses in Clinical Trials \nYouTube video now available.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-lorenzo-trippa/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/01/trippa.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210525T130000
DTEND;TZID=America/New_York:20210525T140000
DTSTAMP:20260413T102219
CREATED:20210506T120012Z
LAST-MODIFIED:20211021T134757Z
UID:2724-1621947600-1621951200@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Modeling Cancer Etiology and Evolution\, and its Implications for Prevention
DESCRIPTION:Tuesday May 25\, 2021\n1:00PM ET \nA conversation with Cristian Tomasetti\nAssociate Professor\, Department of Oncology\, Johns Hopkins Medicine\, Sidney Kimmel Comprehensive Cancer Center \nModerator: Giovanni Parmigiani \nYouTube Video now available. 
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-mathematical-modeling-and-cancer-prevention/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2021/05/cristian-head-shot.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210519T100000
DTEND;TZID=America/New_York:20210519T120000
DTSTAMP:20260413T102219
CREATED:20210415T135645Z
LAST-MODIFIED:20210520T125744Z
UID:2703-1621418400-1621425600@ds.dfci.harvard.edu
SUMMARY:Training Session: Efficient Phase I Clinical Trial Design
DESCRIPTION:May 19\, 2021\n10:00am-12:00PM Eastern Time \nFangxin Hong\, PhD\nSenior Research Scientist\nDepartment of Data Science\, Dana-Farber Cancer Institute\nDepartment of Biostatistics\, Harvard T.H. Chan School of Public Health \n  \n 
URL:https://ds.dfci.harvard.edu/event/training-session-efficient-phase-i-clinical-trial-design/
CATEGORIES:Training Session
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2021/04/FH.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210511T130000
DTEND;TZID=America/New_York:20210511T140000
DTSTAMP:20260413T102219
CREATED:20200921T180944Z
LAST-MODIFIED:20210511T180948Z
UID:2138-1620738000-1620741600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Single-Cell RNA-Seq Data Analysis Via a Regularized Zero-Inflated Mixture Model Framework
DESCRIPTION:Frontiers in Biostatistics Seminar\nMay 11\, 2021\n1:00PM \nJianhua Hu\, PhD\nProfessor\, Biostatistics (in Medicine and in the Herbert Irving Comprehensive Cancer Center)\nDirector\, Cancer Biostatistics Program\nColumbia University \nRegister at: http://bit.ly/FIBMay21 \nAbstract: Applications of single-cell RNA sequencing in various biomedical research areas have been blooming. This new technology provides unprecedented opportunities to study disease heterogeneity at the cellular level. However\, unique characteristics of scRNA-seq data\, including large dimensionality\, high dropout rates\, and possibly batch effects\, bring great difficulty into the analysis of such data. Not appropriately addressing these issues obstructs true scientific discovery. Herein\, we propose a unified Regularized Zero-inflated Mixture Model framework designed for scRNA-seq data (RZiMM-scRNA) to simultaneously detect cell subgroups and identify gene differential expression based on a developed importance score\, accounting for both dropouts and batch effects. We conduct extensive empirical investigation to demonstrate the promise of RZiMM-scRNA in comparison to several popular methods\, including K-means and Hierarchical clustering. \nThis seminar was not recorded as the research presented has not been published yet. 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-jianhua-hu/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/Hu_4x4.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210323T130000
DTEND;TZID=America/New_York:20210323T140000
DTSTAMP:20260413T102219
CREATED:20210205T151853Z
LAST-MODIFIED:20210511T181115Z
UID:2447-1616504400-1616508000@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: The Importance of Representative Samples in Clinical Trials
DESCRIPTION:Tuesday March 23\, 2021\n1:00PM Eastern Time \nA conversation with Timothy Rebbeck\nVincent L. Gregory\, Jr. Professor of Cancer Prevention\, Epidemiology\, Harvard T.H. Chan School Of Public Health\nProfessor\, Medical Oncology\, Dana-Farber Cancer Institute \nModerator: Rafael Irizarry \nYouTube Link: https://www.youtube.com/watch?v=Q4uBibxGf20
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-the-importance-of-representative-samples-in-clinical-trials/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/02/rebbeck_sq.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210309T130000
DTEND;TZID=America/New_York:20210309T140000
DTSTAMP:20260413T102219
CREATED:20200921T180427Z
LAST-MODIFIED:20210511T181203Z
UID:2133-1615294800-1615298400@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Distributed Statistical Learning and Inference in EHR and Other Healthcare Datasets
DESCRIPTION:Frontiers in Biostatistics Seminar\nMarch 9\, 2021\n1:00PM \nRui Duan\, PhD\nAssistant Professor of Biostatistics\nHarvard TH Chan School of Public Health \nDistributed Statistical Learning and Inference in EHR and Other Healthcare Datasets  \nAbstract: The growth of availability and variety of healthcare data sources has provided unique opportunities for data integration and evidence synthesis\, which can potentially accelerate knowledge discovery and enable better clinical decision making.  However\, many practical and technical challenges\, such as data privacy\, high-dimensionality and heterogeneity across different datasets\, remain to be addressed. In this talk\, I will introduce several methods for effective and efficient integration of electronic health records and other healthcare datasets. Specifically\, we develop communication-efficient distributed algorithms for jointly analyzing multiple datasets without the need of sharing patient-level data. Our algorithms are able to account for heterogeneity across different datasets. We provide theoretical guarantees for the performance of our algorithms\, and examples of implementing the algorithms to real-world clinical research networks. \nYouTube Link: https://www.youtube.com/watch?v=IscQ3ruxl1o
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-rui-duan/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/duan4x4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210223T130000
DTEND;TZID=America/New_York:20210223T140000
DTSTAMP:20260413T102219
CREATED:20210205T145646Z
LAST-MODIFIED:20210511T181245Z
UID:2441-1614085200-1614088800@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Diversity and Ethics in Genomics
DESCRIPTION:Tuesday February 23\, 2021\n1:00PM Eastern Time \nA conversation with Keolu Fox\, PhD\nAssistant Professor\, Department of Anthropology\, University of California\, San Diego \nModerator: Aedin Culhane \nYouTube Link: https://www.youtube.com/watch?v=VANlStOnFPY
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-diversity-and-ethics-in-genomics/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/02/fox3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210209T130000
DTEND;TZID=America/New_York:20210209T140000
DTSTAMP:20260413T102219
CREATED:20200921T175912Z
LAST-MODIFIED:20210511T181321Z
UID:2129-1612875600-1612879200@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Group Sequential Design Assuming Delayed Benefit
DESCRIPTION:February 9\, 2021\n1:00PM ET \nKeaven Anderson\, PhD\nScientific AVP\, Methodology Research\, Biostatistics at Merck \nGroup Sequential Design Assuming Delayed Benefit \nAbstract: We consider an asymptotic approach to design of group sequential trials with a potentially delayed effects. Logrank\, weighted logrank tests and combination tests are of primary interest\, but we also consider restricted mean survival. The asymptotic approach allows both quick derivation of study design properties that are also easily verified using simulation. We rely heavily on work done by Tsiatis\, 1981 published while he was at the HSPH. The impact of a potential delay in treatment effect on timing of analyses\, study boundaries and sample size are demonstrated. The value of a robust design that is well powered under a variety of assumptions is emphasized. Open source software is provided for implementation. Given the current regulatory climate\, logrank testing may still be preferred for these trials\, but we hope that efficiencies and Type I error control associated with alternatives may make other options more acceptable for future consideration. \nYouTube Link: https://www.youtube.com/watch?v=DhwZOX5uMKU
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-keaven-anderson/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/anderson1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210126T130000
DTEND;TZID=America/New_York:20210126T140000
DTSTAMP:20260413T102219
CREATED:20210119T133552Z
LAST-MODIFIED:20210511T181357Z
UID:2413-1611666000-1611669600@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Vaccine Prioritization Strategies
DESCRIPTION:Tuesday January 26\, 2021\n1:00PM ET \nA conversation with Daniel Larremore\nAssistant Professor\, Department of Computer Science at University of Colorado-Boulder and BioFrontiers Institute \nand \nKate Bubar\nStudent\, Department of Applied Mathematics\, University of Colorado-Boulder \nModerator: Rafael Irizarry \nRSVP at https://bit.ly/DSJan26 \nYouTube Link: https://www.youtube.com/watch?v=fJuHNNP8TLg
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-vaccine-prioritization-strategies/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/01/joint_pix.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210112T130000
DTEND;TZID=America/New_York:20210112T140000
DTSTAMP:20260413T102219
CREATED:20200921T175311Z
LAST-MODIFIED:20210112T190625Z
UID:2123-1610456400-1610460000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Statistical Modeling and Adjustment for Sampling Biases
DESCRIPTION:Frontiers in Biostatistics Seminar\nJanuary 12\, 2021\n1:00PM \nJing Ning\, PhD\nAssociate Professor. Department of Biostatistics\nDivision of Quantitative Sciences\nThe University of Texas M.D. Anderson Cancer Center \nRegister at: https://bit.ly/FIBJan12 \nAbstract: Bias sampling mechanisms are commonly encountered in applications where the subjects in a target population are not given an equal chance to be selected\, either accidentally\, by natural circumstances\, or intentionally by design. Statistical methods not properly accounting for such a challenge often lead to invalid inferences. For example\, evidence combined from published studies may lead to overly optimistic conclusions due to publication bias\, and the well-known length bias can cause the screening to appear to be more successful than it really is. In this talk\, I will present our recent work to adjust the sampling biases in diverse applications such as the survivorship bias in prevalent cohort\, the self-reporting bias in longitudinal analysis and the publication bias in meta-analysis.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-jing-ning/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201221T140000
DTEND;TZID=America/New_York:20201221T150000
DTSTAMP:20260413T102219
CREATED:20201211T185808Z
LAST-MODIFIED:20201222T214128Z
UID:2361-1608559200-1608562800@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Understanding COVID-19 Vaccine Trial Designs and Current Results
DESCRIPTION:Monday December 21\, 2020\n2:00PM ET \nUnderstanding COVID-19 Vaccine Trial Designs and Current Results \nA conversation with David Benkeser\nAssistant Professor\, Biostatistics and Bioinformatics\nRollins School of Public Health\, Emory University \nModerator: Rafael Irizarry \nRSVP at https://bit.ly/DSDec21
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-understanding-covid-19-vaccine-trial-designs-and-current-results/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/12/benkeser_sq.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201215T130000
DTEND;TZID=America/New_York:20201215T140000
DTSTAMP:20260413T102219
CREATED:20201119T201351Z
LAST-MODIFIED:20201215T204139Z
UID:2324-1608037200-1608040800@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Data-Driven Policy in Puerto Rico
DESCRIPTION:Tuesday December 15\, 2020\n1:00PM ET \nData-Driven Policy in Puerto Rico \nA conversation with Arnaldo Cruz\nDirector of Research and Policy of the Financial\nOversight and Management Board of Puerto Rico\nCo-founder of ABRE Puerto Rico \nModerator: Rafael Irizarry \nRSVP at https://bit.ly/DSDec15
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-data-driven-policy-in-puerto-rico/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/11/cruz.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201214T150000
DTEND;TZID=America/New_York:20201214T163000
DTSTAMP:20260413T102219
CREATED:20201130T190048Z
LAST-MODIFIED:20201215T114236Z
UID:2341-1607958000-1607963400@ds.dfci.harvard.edu
SUMMARY:DF/HCC Cancer Data Science Program &Harvard Chan Bioinformatics CoreJoint Symposium on scRNAseq Methodology
DESCRIPTION:Monday\, December 14\, 3:00-4:30 PM ET  \nRSVP https://bit.ly/CDSBioDec14 \nSpeakers: \n\nAedin Culhane\, Senior Research Scientist\, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health\nIsabella Grabski\, PhD Student in Biostatistics\, Harvard University\nProbabilistic gene barcodes identify cell-types in single-cell RNA-sequencing data\nShannan Ho Sui\, Senior Research Scientist\, Harvard T.H. Chan School of Public Health\nRadhika S Khetani\, Research Scientist\, Harvard T.H. Chan School of Public Health\nPeter Kharchenko\, Associate Professor of Biomedical Informatics\, Harvard Medical School\nX. Shirley Liu\, Professor\, Harvard T.H. Chan School of Public Health\nAnalysis of Single-Cell Data to Model Tumor and Immune Gene Regulation\nLuca Pinello\, Associate Professor of Pathology\, Harvard Medical School and Mass General Cancer Center\nTrajectory inference and visualization of single cell data
URL:https://ds.dfci.harvard.edu/event/df-hcc-cancer-data-science-program-harvard-chan-bioinformatics-corejoint-symposium-on-scrnaseq-methodology/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201208T130000
DTEND;TZID=America/New_York:20201208T140000
DTSTAMP:20260413T102219
CREATED:20201113T144308Z
LAST-MODIFIED:20201209T120537Z
UID:2302-1607432400-1607436000@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Data Science Books\, Online Courses\, Podcasts\, and Blogs
DESCRIPTION:Tuesday December 8\, 2020\n1:00PM ET \nData Science Books\, Online Courses\, Podcasts\, and Blogs \nA conversation with\nRoger Peng\, PhD\nJohns Hopkins Bloomberg School of Public Health \nModerator: Rafael Irizarry \nRSVP at https://bit.ly/DSDec8
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-data-science-books-online-courses-podcasts-and-blogs/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/11/peng7_square.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201207T130000
DTEND;TZID=America/New_York:20201207T143000
DTSTAMP:20260413T102219
CREATED:20201111T004449Z
LAST-MODIFIED:20201208T005647Z
UID:2276-1607346000-1607351400@ds.dfci.harvard.edu
SUMMARY:Postdoctoral Open House
DESCRIPTION:The Dana-Farber Cancer Institute Department of Data Science announces our first annual Postdoc Open House. Join us on Monday\, December 7th at 1:00PM EST to explore postdoctoral opportunities in cancer research. Participating faculty include:  \n\nRafael Irizarry\, PhD\, Professor and Department Chair\nX. Shirley Liu\, PhD\, Professor\nGiovanni Parmigiani\, PhD\, Professor\nFranziska Michor\, PhD\, Professor\nLorenzo Trippa\, PhD\, Associate Professor\nHeng Li\, PhD\, Assistant Professor\nSahand Hormoz\, PhD\, Assistant Professor\nNabihah Tayob\, PhD\, Member of the Faculty\n\nYou will also meet current and former department postdocs\, and learn about the resources available at Dana-Farber and throughout the Boston area.  \nDue to limited space\, we request that participants are actively seeking a postdoctoral position. Please complete this form to request event admission. \nWe look forward to welcoming you and introducing our department!  \nOpen postdoctoral positions are posted on our website.
URL:https://ds.dfci.harvard.edu/event/postdoctoral-open-house/
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/11/website_square.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201117T130000
DTEND;TZID=America/New_York:20201117T140000
DTSTAMP:20260413T102219
CREATED:20201013T194550Z
LAST-MODIFIED:20201117T190533Z
UID:2191-1605618000-1605621600@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Communicating Statistical Findings Effectively
DESCRIPTION:Tuesday November 17\, 2020\n1:00PM ET \nCommunicating Statistical Findings Effectively \nA conversation with\nProfessor Sir David Spiegelhalter\nChair\, Winton Centre for Risk and Evidence Communication\nCentre for Mathematical Sciences\nAuthor: The Art of Statistics \nModerator: Rafael Irizarry \nRSVP at https://bit.ly/DSNov17
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-david-spiegelhalter/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/10/spiegelhalter_square.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201110T130000
DTEND;TZID=America/New_York:20201110T140000
DTSTAMP:20260413T102219
CREATED:20201030T123107Z
LAST-MODIFIED:20201111T005540Z
UID:2226-1605013200-1605016800@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Election Forecasting: How We Succeeded Brilliantly\, Failed Miserably\, or Landed Somewhere in Between
DESCRIPTION:Tuesday November 10\, 2020\n1:00PM ET \nElection Forecasting: How We Succeeded Brilliantly\, Failed Miserably\, or Landed Somewhere in Between \nA conversation with\nAndrew Gelman\, Ph.D.\nProfessor of Statistics and Political Science\nColumbia University \nModerator: Rafael Irizarry \nRSVP at https://bit.ly/DSNov10
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-election-forecasting-how-we-succeeded-brilliantly-failed-miserably-or-landed-somewhere-in-between/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/10/gelman_square.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201020T130000
DTEND;TZID=America/New_York:20201020T140000
DTSTAMP:20260413T102219
CREATED:20200921T155020Z
LAST-MODIFIED:20201109T234121Z
UID:2118-1603198800-1603202400@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Data Science and Academic Leadership
DESCRIPTION:Tuesday October 20\, 2020\n1:00PM ET \nData Science and Academic Leadership \nA conversation with F. DuBois Bowman\, PhD\nDean of the University of Michigan School of Public Health \nModerator: Rafael Irizarry \nVideo available on our YouTube Channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-f-dubois-bowman/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/SPH_Dean_DuBois_Bowman_4x4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201015T130000
DTEND;TZID=America/New_York:20201015T140000
DTSTAMP:20260413T102219
CREATED:20201008T172433Z
LAST-MODIFIED:20201019T155448Z
UID:2174-1602766800-1602770400@ds.dfci.harvard.edu
SUMMARY:Computational Biology of DNA Repair in Cancer
DESCRIPTION:Data Science Seminar \nOctober 15\, 2020\n1:00PM ET \nDominik Glodzik\, PhD\nRepare Therapeutics \nZoom link: https://bit.ly/DSOct15 \nAbstract: Whole genome sequences contain within them signatures of mutational processes. In particular\, some of the mutation signatures relate to impaired DNA-repair in cancer cells. Accurate measurement of mutation signatures reveals the role of DNA-repair deficiencies in etiology and progression of cancer. \nWe extended the computational methods for analysis of mutation signatures in order to describe patterns of chromosomal rearrangements. In particular\, the rearrangement signatures enable the assessment of proficiency of homologous recombination (HR). HRDetect\, an algorithm we developed\, predicts probability of HR-deficiency\, and is based on holistic portrayal of mutational signatures across different classes of somatic mutations. Around 20% of breast cancers contain signatures of HR-deficiency\, and this group is wider than the group of carriers of BRCA1/2 mutations. By contrast to adult cancers\, pediatric cancers with known DNA-repair defects display variation of mutational signatures\, hinting at tissue-specificity of mutational signatures. Finally\, in the chromosomally unstable cancers\, we identified structural rearrangements\, in coding and non-coding regions\, that can act as cancer drivers. Altogether\, these results indicate that computational assessment of DNA-repair capacity of tumor cells is now possible. The methods will be crucial to understanding of the DNA-repair mechanisms and tissue-specificity of mutational processes. \nBio: Dominik Glodzik received his PhD in Computational Biology from the University of Edinburgh\, and held a postdoctoral position at Wellcome Trust Sanger Institute\, before moving to a staff scientist position at Memorial Sloan Kettering Cancer Center. Currently he is a Principal Bioinformatician at Repare Therapeutics in Cambridge.
URL:https://ds.dfci.harvard.edu/event/computational-biology-of-dna-repair-in-cancer/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/10/glodzik.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201013T130000
DTEND;TZID=America/New_York:20201013T140000
DTSTAMP:20260413T102219
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
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END:VEVENT
END:VCALENDAR