BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Dana-Farber Cancer Institute - ECPv6.15.13//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://ds.dfci.harvard.edu
X-WR-CALDESC:Events for Dana-Farber Cancer Institute
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221101T130000
DTEND;TZID=America/New_York:20221101T140000
DTSTAMP:20260407T210758
CREATED:20221013T144710Z
LAST-MODIFIED:20221101T194024Z
UID:3821-1667307600-1667311200@ds.dfci.harvard.edu
SUMMARY:Design and Implementation of Bayesian Adaptive Phase I Trials in Oncology using the DEDUCE Application
DESCRIPTION:Frontiers in Biostatistics Seminar Series \nTuesday November 1\, 2022\n1:00PM Eastern Time \nRegister for in-person or virtual attendance. \nWendy London\, PhD\nAssociate Professor\, Harvard Medical School\nDirector of Biostatistics\, Boston Children’s Hospital \nClement Ma\, PhD Assistant Professor\, University of Toronto\nIndependent Scientist\, CAMH
URL:https://ds.dfci.harvard.edu/event/design-and-implementation-of-bayesian-adaptive-phase-i-trials-in-oncology-using-the-deduce-application/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/10/dual_photo.png
GEO:42.3394159;-71.104234
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Center of Life Sciences Room 11081 3 Blackfan Circle Boston MA 02215 United States;X-APPLE-RADIUS=500;X-TITLE=3 Blackfan Circle:geo:-71.104234,42.3394159
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221206T130000
DTEND;TZID=America/New_York:20221206T140000
DTSTAMP:20260407T210758
CREATED:20221013T170456Z
LAST-MODIFIED:20221207T142413Z
UID:3837-1670331600-1670335200@ds.dfci.harvard.edu
SUMMARY:COVID Data-Driven Policy: NC DHHS's Use of Data for Pandemic Response
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday December 6\, 2022\n1:00PM Eastern Time \nRegister for Zoom link. \nJessie Tenenbaum\, PhD\nshe / her / hers\nChief Data Officer\nNC Department of Health and Human Services
URL:https://ds.dfci.harvard.edu/event/covid-data-driven-policy-nc-dhhss-use-of-data-for-pandemic-response/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/10/Tenenbaum-square-copy.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230124T130000
DTEND;TZID=America/New_York:20230124T140000
DTSTAMP:20260407T210758
CREATED:20221128T192916Z
LAST-MODIFIED:20230203T145004Z
UID:3888-1674565200-1674568800@ds.dfci.harvard.edu
SUMMARY:A Nonparametric Bayesian Approach to Use RWD in Clinical Trial Design
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday January 24\, 2023\n1:00PM Eastern Time\nClick to watch the YouTube video. \nPeter Mueller\, PhD\nProfessor\nDepartment of Statistics and Data Sciences\nDepartment of Mathematics\nUniversity of Texas at Austin \n 
URL:https://ds.dfci.harvard.edu/event/a-nonparametric-bayesian-approach-to-use-rwd-in-clinical-trial-design/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/11/mueller.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230214T130000
DTEND;TZID=America/New_York:20230214T140000
DTSTAMP:20260407T210758
CREATED:20221128T194757Z
LAST-MODIFIED:20230307T152329Z
UID:3899-1676379600-1676383200@ds.dfci.harvard.edu
SUMMARY:Using the Case Study of Atezolizumab Development to Rethink Early Phase Oncology Trial Design
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday February 14\, 2023\n1:00PM Eastern Time\nYouTube video \nEmily Zabor\, DrPH\nAssistant Staff Biostatistician\nDepartment of Quantitative Health Sciences at the Cleveland Clinic\, with a joint appointment in the Taussig Cancer Institute\nAssistant Professor of Medicine\, the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-emily-zabor/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/11/emily-touched-8-crop-scaled-e1669664843585.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230302T130000
DTEND;TZID=America/New_York:20230302T140000
DTSTAMP:20260407T210758
CREATED:20230224T194353Z
LAST-MODIFIED:20230308T124839Z
UID:4088-1677762000-1677765600@ds.dfci.harvard.edu
SUMMARY:Computation of High-Dimensional Penalized Generalized Linear Mixed Models
DESCRIPTION:Thursday March 2\, 2023\n1:00pm ET \nHillary Heiling\nBiostatistics PhD candidate\nUniversity of North Carolina Chapel Hill. \nAdd to calendar \nHillary’s statistical focus has primarily been in cancer-related research\, both through her graduate research assistant role in the Lineberger Comprehensive Cancer Center as well as her personal research applications. In cancer research as well as other biomedical areas\, there are issues with the replicability of results between studies. Hillary will present her work on improving the replicable selection of gene signatures for the prediction of cancer subtypes by combining data across studies and performing variable selection on generalized linear mixed models. Hillary has been able to extend the feasible dimensionality of the application to hundreds of predictors by using a factor model decomposition on the random effects\, which behaves as a dimension reduction technique. Hillary has developed software to perform this task and has published her ‘glmmPen’ R package on CRAN.
URL:https://ds.dfci.harvard.edu/event/computation-of-high-dimensional-penalized-generalized-linear-mixed-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/02/hillary-e1677267404626.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230329T130000
DTEND;TZID=America/New_York:20230329T140000
DTSTAMP:20260407T210758
CREATED:20230120T174649Z
LAST-MODIFIED:20230329T184122Z
UID:3949-1680094800-1680098400@ds.dfci.harvard.edu
SUMMARY:Dimension Reduction of Longitudinal Microbiome Data
DESCRIPTION:Frontiers in Biostatistics Seminar \nWednesday March 29\, 2023\n1:00PM Eastern Time \nPixu Shi\, PhD\nAssistant Professor of Biostatistics & Bioinformatics\nDivision of Integrative Genomics\nDepartment of Biostatistics & Bioinformatics\nDuke University School of Medicine \nAbstract:\nThe analysis of longitudinal microbiome is crucial to the understanding of how microbiome changes over time. It often requires careful maneuver of high dimensional microbial features\, missing samples and varying time points across subjects. Longitudinal microbiome data can often be formatted into a high-dimensional order-3 tensor with three modes representing the subject\, time\, and bacteria respectively. In this talk\, we present functional tensor SVD\, a dimension reduction tool that can uncover sub-population structures in subjects\, compress high-dimensional features into low-dimensional trajectories\, and extract shared temporal patterns among features\, all without imputation of missing samples or rounding of time points. We will demonstrate the robust performance of our method through simulations and multiple case studies.
URL:https://ds.dfci.harvard.edu/event/dimension-reduction-of-longitudinal-microbiome-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/01/Pixu_profile_square.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230509T130000
DTEND;TZID=America/New_York:20230509T140000
DTSTAMP:20260407T210758
CREATED:20230410T125338Z
LAST-MODIFIED:20230515T123922Z
UID:4170-1683637200-1683640800@ds.dfci.harvard.edu
SUMMARY:Model-robust and Efficient Covariate Adjustment for Cluster-randomized Experiments
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday May 9\, 2023\n1:00PM Eastern Time \nJoin the Zoom. \nFan Li\, PhD\nAssistant Professor of Biostatistics\, Yale School of Public Health \nCluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions\, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However\, the validity of model-based covariate adjustment is unclear when the working models are misspecified\, leading to ambiguity of estimands and risk of bias. In this article\, we first adapt two conventional model-based methods\, generalized estimating equations and linear mixed models\, with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. Furthermore\, we propose an efficient estimator for each estimand that allows for flexible covariate adjustment and additionally addresses cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and\, if ignored\, can lead to bias of model-based estimators. For our proposed estimator\, we prove that when the nuisance functions are consistently estimated by machine learning algorithms\, the estimator is consistent\, asymptotically normal\, and efficient. When the nuisance functions are estimated via parametric working models\, the estimator is triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives.
URL:https://ds.dfci.harvard.edu/event/model-robust-and-efficient-covariate-adjustment-for-cluster-randomized-experiments/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/04/fanli-scaled-e1681131195600.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230926T100000
DTEND;TZID=America/New_York:20230926T110000
DTSTAMP:20260407T210758
CREATED:20230825T175051Z
LAST-MODIFIED:20231005T140106Z
UID:4458-1695722400-1695726000@ds.dfci.harvard.edu
SUMMARY:Forecasting pancreatic carcinogenesis from spatial multi-omics
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday September 26\, 2023 @ 10am ET\nCenter for Life Sciences Building\, Room 11081 \nElana Fertig\, PhD\nDivision Director of Oncology Quantitative Sciences\, Professor of Oncology\nJohns Hopkins University \nYouTube Video \nCombining genomics with mathematical modeling provides a forecast system that can yield computational predictions to anticipate cancer progression and therapeutic response. High-throughput profiling technologies can indicate the molecular and cellular pathways of malignancies\, but not the effect of targeting those pathways with therapy. Precision interception requires relating therapies to the cellular phenotypes underlying pancreatic carcinogenesis. This talk presents a hybrid computational and experimental strategy to uncover interactions between neoplastic cells and the microenvironment during pancreatic carcinogenesis. As pancreatic cancer develops\, it forms a complex microenvironment of multiple interacting cells. The microenvironment of advanced pancreatic cancer includes a heterogeneous and dense population of cells\, such as macrophages and fibroblasts\, that are associated with immunosuppression. New single-cell and spatial molecular profiling technologies enable unprecedented characterization of the cellular and molecular composition of the microenvironment. These technologies provide the potential to identify candidate therapeutics to intercept immunosuppression in pancreatic cancer. State-of-the-art mathematical approaches in computational biology are essential to uncover mechanistic insights for high-throughput data for these precision interception strategies. \n  \nWant to get our weekly events newsletter? Click here!
URL:https://ds.dfci.harvard.edu/event/forecasting-pancreatic-carcinogenesis-from-spatial-multi-omics/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/08/headshot-copy.jpg
GEO:42.3394159;-71.104234
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Center of Life Sciences Room 11081 3 Blackfan Circle Boston MA 02215 United States;X-APPLE-RADIUS=500;X-TITLE=3 Blackfan Circle:geo:-71.104234,42.3394159
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231024T130000
DTEND;TZID=America/New_York:20231024T140000
DTSTAMP:20260407T210758
CREATED:20230825T175528Z
LAST-MODIFIED:20231031T115135Z
UID:4463-1698152400-1698156000@ds.dfci.harvard.edu
SUMMARY:AI in Medical Imaging: Current State & Future Opportunities
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday October 24\, 2023 @ 1pm ET\nCenter for Life Sciences Building\, Room 11081 \nWilliam Lotter\, PhD\nAssistant Professor\, Dana-Farber Cancer Institute and Harvard Medical School \nYouTube Video \nWant to get our weekly events newsletter? Click here!
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-william-lotter/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/08/Bill_Lotter_headshot-scaled-e1659653586370.jpg
GEO:42.3394159;-71.104234
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Center of Life Sciences Room 11081 3 Blackfan Circle Boston MA 02215 United States;X-APPLE-RADIUS=500;X-TITLE=3 Blackfan Circle:geo:-71.104234,42.3394159
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231108T120000
DTEND;TZID=America/New_York:20231108T130000
DTSTAMP:20260407T210758
CREATED:20231030T131716Z
LAST-MODIFIED:20231030T131847Z
UID:4616-1699444800-1699448400@ds.dfci.harvard.edu
SUMMARY:Single-cell Multiomic Exploration of Oncogenic and Immunologic Programs in Melanoma and CLL
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday November 8th\, 2023\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nJeremy Simon\nSenior Research Scientist\, Harvard T.H. Chan School of Public Health \nLunch is provided. \n 
URL:https://ds.dfci.harvard.edu/event/single-cell-multiomic-exploration-of-oncogenic-and-immunologic-programs-in-melanoma-and-cll/
LOCATION:Center for Life Sciences\, Zelen Commons\, 3 Blackfan Circle\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/01/Jeremy-Simon-600x600-1-e1673981068499.jpeg
GEO:42.3394159;-71.104234
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Center for Life Sciences Zelen Commons 3 Blackfan Circle Boston MA 02115;X-APPLE-RADIUS=500;X-TITLE=3 Blackfan Circle:geo:-71.104234,42.3394159
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231205T130000
DTEND;TZID=America/New_York:20231205T140000
DTSTAMP:20260407T210758
CREATED:20230901T114137Z
LAST-MODIFIED:20231208T201749Z
UID:4488-1701781200-1701784800@ds.dfci.harvard.edu
SUMMARY:Analyzing Big EHR Data - Optimal Cox Regression Subsampling Procedure with Rare Events
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday December 5\, 2023 @ 1pm ET\nYouTube Link \nMalka Gorfine\, PhD\nProfessor\, Department of Statistics\,  Tel Aviv University\, Israel \nAbstract: Massive sized survival datasets become increasingly prevalent with the development of the healthcare industry\, and pose computational challenges unprecedented in traditional survival analysis use cases. In this work we analyze the UK-biobank colorectal cancer data with genetic and environmental risk factors\, including a time-dependent coefficient\, which transforms the dataset into “pseudo-observation” form\, thus critically inflating its size. A popular way for coping with massive datasets is downsampling them\, such that the computational resources can be afforded by the researcher. Cox regression has remained one of the most popular statistical models for the analysis of survival data to-date. This work addresses the settings of right censored and possibly left-truncated data with rare events\, such that the observed failure times constitute only a small portion of the overall sample. We propose Cox regression subsampling-based estimators that approximate their full-data partial-likelihood-based counterparts\, by assigning optimal sampling probabilities to censored observations\, and including all observed failures in the analysis. The suggested methodology is applied on the UK-biobank for building a colorectal cancer risk-prediction model\, while reducing the computation time and memory requirements. We establish asymptotic properties under suitable conditions and develop a framework for determining the optimal subsample size. \nWant to get our weekly events newsletter? Click here!
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-seminar-malka-gorfine/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/09/portrait.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T120000
DTEND;TZID=America/New_York:20231206T130000
DTSTAMP:20260407T210758
CREATED:20231121T123117Z
LAST-MODIFIED:20231121T123117Z
UID:4661-1701864000-1701867600@ds.dfci.harvard.edu
SUMMARY:The Splicing Factor CCAR1 Regulates the Fanconi Anemia/BRA Pathway
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday December 6th\, 2023\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nHuy Nguyen\nComputational Biologist\, Center for DNA Damage & Repair\, Dana-Farber Cancer Institute \nLunch is provided. \n 
URL:https://ds.dfci.harvard.edu/event/the-splicing-factor-ccar1-regulates-the-fanconi-anemia-bra-pathway/
LOCATION:Center for Life Sciences\, Zelen Commons\, 3 Blackfan Circle\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2023/11/huy_square.png
GEO:42.3394159;-71.104234
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Center for Life Sciences Zelen Commons 3 Blackfan Circle Boston MA 02115;X-APPLE-RADIUS=500;X-TITLE=3 Blackfan Circle:geo:-71.104234,42.3394159
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240110T120000
DTEND;TZID=America/New_York:20240110T130000
DTSTAMP:20260407T210758
CREATED:20240104T202615Z
LAST-MODIFIED:20240104T202615Z
UID:4773-1704888000-1704891600@ds.dfci.harvard.edu
SUMMARY:ImmunoPROFILE: An Multiplex Immunofluorescence Based Immune Cell Profiling Test and Data Resource; An Overview and Analysis Vignette Using GNNs
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday January 10th\, 2023\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nKatharina Hoebel\, Research Fellow\, Department of Data Science\, Dana-Farber Cancer Institute\nJames Lindsay\, Director\, Software Engineer\, Department of Data Science\, Dana-Farber Cancer Institute \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/immunoprofile-an-multiplex-immunofluorescence-based-immune-cell-profiling-test-and-data-resource-an-overview-and-analysis-vignette-using-gnns/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/01/katharina_james2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240116T130000
DTEND;TZID=America/New_York:20240116T140000
DTSTAMP:20260407T210758
CREATED:20230927T113715Z
LAST-MODIFIED:20240119T180817Z
UID:4572-1705410000-1705413600@ds.dfci.harvard.edu
SUMMARY:Methods for the Analysis of Data with Missing Values
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday January 16\, 2023 @ 1pm ET\nCenter for Life Sciences Building\, Room 11081 \nRoderick Little\, PhD\nRichard D. Remington Distinguished University Professor of Biostatistics\nProfessor\, Department of Statistics\nResearch Professor\, Institute for Social Research\nUniversity of Michigan School of Public Health \nYouTube Video \nAbstract: I review methods for handling missing data in empirical studies. I define missing data\, provide a taxonomy of main approaches to analysis\, including complete-case and available-case analysis\, weighting\, maximum likelihood (ML)\, Bayes\, single and multiple imputation (MI)\, and augmented inverse-probability weighting (AIPW). Assumptions about the missingness mechanism are key to the performance of alternative methods; I define missingness mechanisms\, which play a key role in the performance of methods. Approaches to robust inference\, and to inference when the mechanism is potentially missing not at random\, are discussed. I’ll also discuss recent developments in the treatment of missing data in interventional studies\, as evidenced by the International Council for Harmonization E9 Addendum on estimands. \nWant to get our weekly events newsletter? Click here!
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-seminar-roderick-little/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/09/rlittle_crop.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240124T120000
DTEND;TZID=America/New_York:20240124T130000
DTSTAMP:20260407T210758
CREATED:20240111T154411Z
LAST-MODIFIED:20240201T125325Z
UID:4827-1706097600-1706101200@ds.dfci.harvard.edu
SUMMARY:Tracing the Mutational Footprints of Cancer to Guide Personalized Therapy
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday January 24th\, 2023\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nDoğa Gülhan\, Principal Investigator\, Mass General Cancer Center\, KF-CCR \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/tracing-the-mutational-footprints-of-cancer-to-guide-personalized-therapy/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/01/gulhan-square.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240206T130000
DTEND;TZID=America/New_York:20240206T140000
DTSTAMP:20260407T210758
CREATED:20240126T132133Z
LAST-MODIFIED:20240209T201454Z
UID:4874-1707224400-1707228000@ds.dfci.harvard.edu
SUMMARY:Quantitative Methods in Implementation Research: Concepts\, Goals\, and Applications
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday February 6\, 2024 @ 1pm ET\nCenter for Life Sciences Building\, Room 11081 \nYou Tube Link \nDonna Spiegelman\, ScD\nSusan Dwight Bliss Professor of Biostatistics and Professor of Cardiovascular Medicine\, Yale School of Medicine Professor\, Department of Statistics and Data Science\, Yale University \nThis talk will provide an overview of the key concepts and goals of quantitative methods that are popular in implementation research\, with a wide range of real-world applications. Randomized\, quasi-experimental and observational designs will be covered.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-donna-spiegelman/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/01/spiegelman-crop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240207T120000
DTEND;TZID=America/New_York:20240207T130000
DTSTAMP:20260407T210758
CREATED:20240129T134117Z
LAST-MODIFIED:20240209T142125Z
UID:4891-1707307200-1707310800@ds.dfci.harvard.edu
SUMMARY:Reconstructing the Chronology of Somatic Events From Precursor Conditions to Advanced Stage Myeloma
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday February 7th\, 2024\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nMehmet Samur\, Senior Research Scientist\, Department of Data Science \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/reconstructing-the-chronology-of-somatic-events-from-precursor-conditions-to-advanced-stage-myeloma/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/samur_mehmet.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240301
DTEND;VALUE=DATE:20240302
DTSTAMP:20260407T210758
CREATED:20240209T142504Z
LAST-MODIFIED:20240304T124147Z
UID:4928-1709251200-1709337599@ds.dfci.harvard.edu
SUMMARY:Call for Abstracts: Health Disparities and Cancer
DESCRIPTION:We invite students\, postdocs\, residents\, clinical fellows\, and early career faculty to submit abstracts for consideration as a lightning talk at the 2024 Marvin Zelen Memorial Symposium. Submit via this form by Friday March 1st\, speakers will be notified by Friday March 8th. \n  \nFriday April 5\, 2024\n1:00-5:30PM ET \nYawkey Conference Center\n450 Brookline Ave\, Boston\, MA \nRegistration for in person and virtual attendance. \nInvited speakers: \n\nAdewole Adamson\, MD\, MPP\, Dell Medical School at the University of Texas at Austin\nCynthia Dwork\,PhD\, Harvard University\nNate Fillmore\,PhD\, VA Boston Healthcare System and Harvard Medical School\nCarmen E. Guerra\, MD\, MSCE\, University of Pennsylvania\nJinani Jayasekera\, PhD\, National Institute on Minority Health and Health Disparities\nBogdan Pasaniuc\,PhD\, University of California Los Angeles\n\n 
URL:https://ds.dfci.harvard.edu/event/call-for-abstracts-health-disparities-and-cancer/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/02/Zelen2024_forwebsite_image.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240312T130000
DTEND;TZID=America/New_York:20240312T140000
DTSTAMP:20260407T210758
CREATED:20240104T203211Z
LAST-MODIFIED:20240311T185258Z
UID:4778-1710248400-1710252000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: A Framework for Personalizing the Timing of Surveillance Testing
DESCRIPTION:**This seminar has been canceled. A new date will be announced shortly**\nFrontiers in Biostatistics Seminar \nTuesday March 12\, 2024 @ 1pm ET \n  \nAasthaa Bansal\, PhD\nAssociate Professor\, The Comparative Health Outcomes\, Policy\, and Economics (CHOICE) Institute\, University of Washington\nJoint Associate Professor\, Public Health Sciences Division\nJoint Associate Professor\, Hutchinson Institute for Cancer Outcomes Research (HICOR)\nFred Hutch Cancer Center \nAbstract: Frequent surveillance testing using biomarkers is recommended and routinely conducted in several disease settings. Although surveillance tests provide information about current disease status and present an opportunity to detect disease progression early\, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. I will discuss our recently developed Personalized Risk-Adaptive Surveillance (PRAISE) framework\, a method for embedding dynamic predictions into a sequential decision-making framework to determine the time point at which the next collection of biomarker data would be most valuable. I will demonstrate a preliminary application of the framework in cystic fibrosis and ongoing work in colorectal cancer to develop more cost-effective and personalized surveillance strategies.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-aasthaa-bansal/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/01/aasthaa_headshot_square-scaled-e1708014759804.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240411T160000
DTEND;TZID=America/New_York:20240411T170000
DTSTAMP:20260407T210758
CREATED:20240311T124600Z
LAST-MODIFIED:20240415T133053Z
UID:5053-1712851200-1712854800@ds.dfci.harvard.edu
SUMMARY:Data Integration in Statistical Inference and Risk Prediction
DESCRIPTION:Joint Seminar with Harvard TH Chan School of Public Health \nThursday April 11\, 2024 @ 4pm ET\nFXB Building Room 301 \nYu Shen PhD\nConversation with a Living Legend Professor & Chair ad interim\nDepartment of Biostatistics\nThe University of Texas MD Anderson Cancer Center \nIn comparative effectiveness research and risk prediction for rare types of cancer\, it is desirable to combine multiple sources of data\, e.g.\, the primary cohort data together with aggregate information derived from cancer registry databases. Such integration of data may improve statistical efficiency and accuracy of risk prediction\, but also pose statistical challenges for incomparability between different sources of data. We develop the adaptive estimation procedures\, which used the combined information to determine the degree of information borrowing from the aggregate data of the external resource. We apply the proposed methods to evaluate the long-term effect of several commonly used treatments for inflammatory breast cancer by tumor subtypes\, while combining the inflammatory breast cancer patient cohort at MD Anderson and external data.
URL:https://ds.dfci.harvard.edu/event/data-integration-in-statistical-inference-and-risk-prediction/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/03/yushen-crop.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240417T120000
DTEND;TZID=America/New_York:20240417T130000
DTSTAMP:20260407T210758
CREATED:20240415T133033Z
LAST-MODIFIED:20240423T144756Z
UID:5314-1713355200-1713358800@ds.dfci.harvard.edu
SUMMARY:Deconvolving Clonotype from High-Throughput Perturb-Seq and Novel Splice Identification of Prematurely Terminated mRNA
DESCRIPTION:Elevate @ Eleven CompBio Connections \nWednesday April 17th\, 2024\n12:00 – 1:00 PM\nZelen Commons\, Center for Life Sciences Building \nJared Brown\, PhD\, Research Fellow\, Data Science \n  \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/deconvolving-clonotype-from-high-throughput-perturb-seq-and-novel-splice-identification-of-prematurely-terminated-mrna/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/08/Brown_Jared.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240501T120000
DTEND;TZID=America/New_York:20240501T130000
DTSTAMP:20260407T210758
CREATED:20240415T145532Z
LAST-MODIFIED:20240502T125348Z
UID:5322-1714564800-1714568400@ds.dfci.harvard.edu
SUMMARY:Identifying Transcription Factor Binding using Open Chromatin\, Transcriptome\, and Methylation Data
DESCRIPTION:Elevate @ Eleven CompBio Connections \nWednesday May 1st\, 2024\n12:00 – 1:00 PM\nZelen Commons\, Center for Life Sciences Building \nMichael Hoffman\, PhD\, Senior Scientist and Chair\, Princess Margaret Cancer Centre and Associate Professor\, University of Toronto \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/identifying-transcription-factor-binding-using-open-chromatin-transcriptome-and-methylation-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/04/Hoffman_IMG_3344-cropped-1024x1024-1-e1713192907447.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240507T130000
DTEND;TZID=America/New_York:20240507T140000
DTSTAMP:20260407T210758
CREATED:20240304T124913Z
LAST-MODIFIED:20240507T191511Z
UID:5006-1715086800-1715090400@ds.dfci.harvard.edu
SUMMARY:Cancer Risk Prediction\, Early Detection and Minimal Residual Disease
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday May 7\, 2024 @ 1pm ET \nIn-person and Zoom available. \nCristian Tomasetti\, Ph.D.\nDirector\, Center for Cancer Prevention and Early Detection\, City of Hope\nProfessor and Director\, Division of Mathematics for Cancer Evolution and Early Detection\, Department of Computational & Quantitative Medicine\, City of Hope
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-cristian-tomasetti/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/03/tomasetti-300x300-1-e1709556955216.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240813T130000
DTEND;TZID=America/New_York:20240813T140000
DTSTAMP:20260407T210758
CREATED:20240806T113832Z
LAST-MODIFIED:20240815T144154Z
UID:5477-1723554000-1723557600@ds.dfci.harvard.edu
SUMMARY:Challenges of Producing High-Quality Labelled Data
DESCRIPTION:Tuesday\, August 13 @ 1:00PM Eastern Time\nCenter for Life Sciences Building\, 11th floor\, room 11081\n3 Blackfan Circle\, Boston \nPeter Lipman\nTechnical Lead & Manager\nData Science & Human Computation\, Google \nZoom: https://bit.ly/plipman
URL:https://ds.dfci.harvard.edu/event/challenges-of-producing-high-quality-labelled-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/08/lipman_headshot_crop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240830T080000
DTEND;TZID=America/New_York:20240830T170000
DTSTAMP:20260407T210758
CREATED:20240729T122857Z
LAST-MODIFIED:20240904T112937Z
UID:5455-1725004800-1725037200@ds.dfci.harvard.edu
SUMMARY:Fall schedule to be announced
DESCRIPTION:Watch here for updates on our fall seminar schedule. You can also sign up for our weekly newsletter to stay informed of data science events in the Boston area.
URL:https://ds.dfci.harvard.edu/event/fall-schedule-to-be-announced/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/svg+xml:https://ds.dfci.harvard.edu/wp-content/uploads/2021/02/dfds_logo.svg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240912T160000
DTEND;TZID=America/New_York:20240912T170000
DTSTAMP:20260407T210758
CREATED:20240904T112923Z
LAST-MODIFIED:20240917T172028Z
UID:5503-1726156800-1726160400@ds.dfci.harvard.edu
SUMMARY:Hierarchical Causal Models
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium Series \nThursday September 12\, 2024\n4:00-5:00PM\nHSPH FXB Building Room 301 \nDavid Blei\, PhD\nProfessor of Statistics and Computer Science\nColumbia University \nAnalyzing nested data with hierarchical models is a staple of Bayesian statistics\, but causal modeling remains largely focused on “flat” models. In this talk\, we will explore how to think about nested data in causal models\, and we will consider the advantages of nested data over aggregate data (such as data means) for causal inference. We show that disaggregating your data—replacing a flat causal model with a hierarchical causal model—can provide new opportunities for identification and estimation. \nAs examples\, we will study how to identify and estimate causal effects under unmeasured confounders\, interference\, and instruments. \nThis is joint work with Eli Weinstein.
URL:https://ds.dfci.harvard.edu/event/hierarchical-causal-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/09/blei_headshot_crop.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240919T160000
DTEND;TZID=America/New_York:20240919T170000
DTSTAMP:20260407T210758
CREATED:20240917T172017Z
LAST-MODIFIED:20240920T175443Z
UID:5541-1726761600-1726765200@ds.dfci.harvard.edu
SUMMARY:Inference for Treatment-Specific Survival Curves using Machine Learning
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium Series \nThursday September 19\, 2024\n4:00-5:00PM\nHSPH FXB Building Room 313 \nTed Westling\, Assistant Professor\, Department of Mathematics & Statistics\, University of Massachusetts Amherst \nIn the absence of data from a randomized trial\, researchers often aim to use observational data to draw causal inference about the effect of a treatment on a time-to-event outcome. In this context\, interest often focuses on the treatment-specific survival curves; that is\, the survival curves were the entire population under study to be assigned to receive the treatment or not. Under certain causal conditions\, including that all confounders of the treatment-outcome relationship are observed\, the treatment-specific survival can be identified with a covariate-adjusted survival function. Several estimators of this function have been proposed\, including estimators based on outcome regression\, inverse probability weighting\, and doubly robust estimators. We propose a cross-fitted doubly-robust estimator that incorporates data-adaptive estimators of the conditional survival functions. We establish conditions on the nuisance estimators under which our estimator is consistent and asymptotically linear\, both pointwise and uniformly in time. We also propose an ensemble learner for combining multiple candidate estimators of the conditional survival estimators. Our methods and results accommodate events occurring in discrete or continuous time (or both). We investigate the practical performance of our methods using an application to the effect of a surgical treatment to prevent metastases of parotid carcinoma on mortality. Time permitting\, we will discuss ongoing work concerning sensitivity analysis for survival curves.
URL:https://ds.dfci.harvard.edu/event/inference-for-treatment-specific-survival-curves-using-machine-learning/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/09/Ted-Westling-long.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241018T150000
DTEND;TZID=America/New_York:20241018T160000
DTSTAMP:20260407T210758
CREATED:20241008T113529Z
LAST-MODIFIED:20241022T164100Z
UID:5585-1729263600-1729267200@ds.dfci.harvard.edu
SUMMARY:Empirical Bayes Matrix Factorization\, and Genomic Applications
DESCRIPTION:Data Science Seminar\nFriday\, October 18\, 3:00 PM ET \nCenter for Life Sciences Building\, 11th Floor \nMatthew Stephens\, PhD\nChair\, Department of Statistics; Ralph W. Gerard Professor of Statistics\, Human Genetics\, University of Chicago
URL:https://ds.dfci.harvard.edu/event/empirical-bayes-matrix-factorization-and-genomic-applications/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/10/Stephens_Matthew_600x600-300x300-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241023T120000
DTEND;TZID=America/New_York:20241023T130000
DTSTAMP:20260407T210758
CREATED:20241011T180746Z
LAST-MODIFIED:20241023T172801Z
UID:5608-1729684800-1729688400@ds.dfci.harvard.edu
SUMMARY:Targeting CARM1 in Dendritic Cells for Cancer Immunotherapy
DESCRIPTION:Compbio Connections\n\nOctober 23rd\, 12:00 -1:00 PM ET\nCenter for Life Sciences Building\, 11th floor\, Zelen Commons \nXixi Zhang\, PhD\nResearch Fellow\, Dana-Farber Cancer Institute \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/targeting-carm1-in-dendritic-cells-for-cancer-immunotherapy/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/10/Zhang_Xixi_headshot.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241106T120000
DTEND;TZID=America/New_York:20241106T130000
DTSTAMP:20260407T210758
CREATED:20241029T141932Z
LAST-MODIFIED:20241029T141932Z
UID:5644-1730894400-1730898000@ds.dfci.harvard.edu
SUMMARY:Harnessing Bioinformatics for Cancer Research at DFCI
DESCRIPTION:Compbio Connections\n\nNovember 6\, 12:00 -1:00 PM ET\nCenter for Life Sciences Building\, 11th floor\, Zelen Commons \n\n\n\n\nMichael Tolstorukov\nDirector\, Bioinformatics and Molecular Data\, Informatics and Analytics\, Dana-Farber Cancer Institute \n\n\n\n\nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/harnessing-bioinformatics-for-cancer-research-at-dfci/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2024/10/T_Michael_headshot-e1730211557553.jpg
END:VEVENT
END:VCALENDAR