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DTSTART;TZID=America/New_York:20210112T130000
DTEND;TZID=America/New_York:20210112T140000
DTSTAMP:20260409T030027
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
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/ning.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201214T150000
DTEND;TZID=America/New_York:20201214T163000
DTSTAMP:20260409T030027
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
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/11/dfhhc-joint-symp-icon.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201015T130000
DTEND;TZID=America/New_York:20201015T140000
DTSTAMP:20260409T030027
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:20260409T030027
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:20200929T130000
DTEND;TZID=America/New_York:20200929T140000
DTSTAMP:20260409T030027
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:20260409T030027
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200915T130000
DTEND;TZID=America/New_York:20200915T140000
DTSTAMP:20260409T030027
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:20200526T130000
DTEND;TZID=America/New_York:20200526T140000
DTSTAMP:20260409T030027
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:20260409T030027
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
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/marianne_uts1_crop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200428T130000
DTEND;TZID=America/New_York:20200428T140000
DTSTAMP:20260409T030027
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
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/simon_square.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200402T130000
DTEND;TZID=America/New_York:20200402T140000
DTSTAMP:20260409T030027
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
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