Dimension Reduction of Longitudinal Microbiome Data
2023-03-29 13:00 - 14:00 (America/New_York)
Frontiers in Biostatistics Seminar
Wednesday March 29, 2023
1:00PM Eastern Time
Pixu Shi, PhD
Assistant Professor of Biostatistics & Bioinformatics
Division of Integrative Genomics
Department of Biostatistics & Bioinformatics
Duke University School of Medicine
Abstract:
The 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.

Machine Learning in Medical Imaging: From New Techniques to Clinical Translation
2023-04-07 13:00 - 17:00 | Simmons University, 300 The Fenway, Boston, MA, United States
Zelen 2023 Program
The 2023 Marvin Zelen Memorial Symposium featuring talks by:

Andy Beck (Co-founder & CEO, Path AI)
Emerging Methods in Machine Learning and Applications for Digital Pathology
Jonathan Chen (Instructor in Pathology, MGH)
Multicellular Networks: Immunity Hubs in Human NSCLC
Marzyeh Ghassemi (Assistant Professor, MIT)
Designing Machine Learning Processes For Equitable Health Systems
Polina Golland (Professor, MIT)
Learning to Read X-ray: Applications to Heart Failure Monitoring
Jayashree Kalpathy-Cramer (Professor, University of Colorado, MGH, and HMS)
Opportunities and Challenges for Medical Imaging AI – Lessons from Oncology and Ophthalmology
Bill Lotter (Member of the Faculty, DFCI and HMS)
AI for Mammography: From Algorithms to Clinical Integration
Shantanu Singh (Senior Group Leader, Broad Institute)
Fortune-telling with Images: Painting Cells for Discovering Drugs

Friday April 7, 2023
1:00-5:00PM
Simmons University
Paresky Conference Center
300 The Fenway, Boston, MA
Reception to follow.
Register for virtual or in-person attendance.

Model-robust and Efficient Covariate Adjustment for Cluster-randomized Experiments
2023-05-09 13:00 - 14:00
Frontiers in Biostatistics Seminar
Tuesday May 9, 2023
1:00PM Eastern Time
Join the Zoom.
Fan Li, PhD
Assistant Professor of Biostatistics, Yale School of Public Health
Cluster-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.

BioC2023: the Bioconductor Annual Conference
2023-08-02 - 2023-08-04 | Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, United States
We are incredibly excited that Bioc2023 the annual Bioconductor meeting will be in Boston at the Dana-Farber Cancer Institute from August 2-4. It is a hybrid meeting to enable maximum outreach to our global community. Registration is now open and scholarships are available.
Contact us for sponsorship opportunities.
Speakers include:

JJ Allaire (Founder and CEO of RStudio, Posit PBC)
Heng Li (Associate Professor, Dana-Farber Cancer Institute, Harvard Medical School)
Beth Cimini (Senior Group Leader, Broad Institute of MIT & Harvard)
Jeffrey Moffitt (Assistant Professor, Harvard Medical School and Boston Children’s Hospital)
Sam Lent (Senior Computational Biologist at Freenome)

Connect with talented R/Bioconductor users & developers, data scientists, biostatisticians, and bioinformaticians at #Bioc2023

Forecasting pancreatic carcinogenesis from spatial multi-omics
2023-09-26 10:00 - 11:00 | Center of Life Sciences, Room 11081, 3 Blackfan Circle, Boston, MA, 02215, United States
Frontiers in Biostatistics Seminar
Tuesday September 26, 2023 @ 10am ET
Center for Life Sciences Building, Room 11081
Elana Fertig, PhD
Division Director of Oncology Quantitative Sciences, Professor of Oncology
Johns Hopkins University
YouTube Video
Combining 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.

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AI in Medical Imaging: Current State & Future Opportunities
2023-10-24 13:00 - 14:00 | Center of Life Sciences, Room 11081, 3 Blackfan Circle, Boston, MA, 02215, United States
Frontiers in Biostatistics Seminar
Tuesday October 24, 2023 @ 1pm ET
Center for Life Sciences Building, Room 11081
William Lotter, PhD
Assistant Professor, Dana-Farber Cancer Institute and Harvard Medical School
YouTube Video
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Single-cell Multiomic Exploration of Oncogenic and Immunologic Programs in Melanoma and CLL
2023-11-08 12:00 - 13:00 | Center for Life Sciences, Zelen Commons, 3 Blackfan Circle, Boston, MA, 02115
Elevate @ Eleven: Comp Bio Connections
Wednesday November 8th, 2023
12:00-1:00PM
In-person only, Zelen Commons, 11th floor of the Center for Life Sciences Building
Jeremy Simon
Senior Research Scientist, Harvard T.H. Chan School of Public Health
Lunch is provided.

Analyzing Big EHR Data - Optimal Cox Regression Subsampling Procedure with Rare Events
2023-12-05 13:00 - 14:00
Frontiers in Biostatistics Seminar
Tuesday December 5, 2023 @ 1pm ET
YouTube Link
Malka Gorfine, PhD
Professor, Department of Statistics, Tel Aviv University, Israel
Abstract: 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.
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The Splicing Factor CCAR1 Regulates the Fanconi Anemia/BRA Pathway
2023-12-06 12:00 - 13:00 | Center for Life Sciences, Zelen Commons, 3 Blackfan Circle, Boston, MA, 02115
Elevate @ Eleven: Comp Bio Connections
Wednesday December 6th, 2023
12:00-1:00PM
In-person only, Zelen Commons, 11th floor of the Center for Life Sciences Building
Huy Nguyen
Computational Biologist, Center for DNA Damage & Repair, Dana-Farber Cancer Institute
Lunch is provided.

ImmunoPROFILE: An Multiplex Immunofluorescence Based Immune Cell Profiling Test and Data Resource; An Overview and Analysis Vignette Using GNNs
2024-01-10 12:00 - 13:00
Elevate @ Eleven: Comp Bio Connections
Wednesday January 10th, 2023
12:00-1:00PM
In-person only, Zelen Commons, 11th floor of the Center for Life Sciences Building
Katharina Hoebel, Research Fellow, Department of Data Science, Dana-Farber Cancer Institute
James Lindsay, Director, Software Engineer, Department of Data Science, Dana-Farber Cancer Institute
Lunch is provided.

Methods for the Analysis of Data with Missing Values
2024-01-16 13:00 - 14:00
Frontiers in Biostatistics Seminar
Tuesday January 16, 2023 @ 1pm ET
Center for Life Sciences Building, Room 11081
Roderick Little, PhD
Richard D. Remington Distinguished University Professor of Biostatistics
Professor, Department of Statistics
Research Professor, Institute for Social Research
University of Michigan School of Public Health
YouTube Video
Abstract: 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.
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Tracing the Mutational Footprints of Cancer to Guide Personalized Therapy
2024-01-24 12:00 - 13:00
Elevate @ Eleven: Comp Bio Connections
Wednesday January 24th, 2023
12:00-1:00PM
In-person only, Zelen Commons, 11th floor of the Center for Life Sciences Building
Doğa Gülhan, Principal Investigator, Mass General Cancer Center, KF-CCR
Lunch is provided.

DF/HCC Celebration of Early Career Investigators in Cancer Research
2024-01-31 13:00 - 17:30
Wednesday January 31, 2024
1:00pm – 5:30PM
Yawkey Conference Center, Dana-Farber Cancer Institute
Registration.
2024 Keynote Speaker: Jeff Leek, PhD
Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics at the Fred Hutchinson Cancer Center
Talk Title: “Taking Calculated Career Risks to Impact the World Through Data Science”
Introductions by:

Ramesh Shivdasani, Deputy Director of the DF/HCC and Professor of Medicine at Harvard Medical School, and
Laurie Glimcher, Director of the DF/HCC and President and CEO of the Dana-Farber Cancer Institute

Early career investigators are a unique reservoir of new ideas, innovation, and excellence in cancer research. To celebrate this, we welcome you to join the 11th Annual DF/HCC Celebration of Early Investigators in Cancer Research. This symposium will showcase the talent of early career investigators at the Dana-Farber/Harvard Cancer Center (DF/HCC) who work in several areas of population science, including epidemiology, biostatistics, outcomes, diversity, and survivorship. We invite all members of the public to attend the event. We also invite students, post-docs, residents, clinical fellows, and early career investigators to submit abstracts for consideration as oral or poster presentations. We look forward to an exciting afternoon of discussion, sharing new discoveries, and building new collaborations. – Lorelei Mucci, ScD (HSPH), Giovanni Parmigiani, PhD (DFCI), Erica Feick (DFCI), Bailey Vaselkiv (HSPH), and Hannah Guard (HSPH).
Currently, the event will be broadcast live without any recording option. All speakers must attend in person. Adjustments to the event status will be considered if there are changes to masking regulations.

Quantitative Methods in Implementation Research: Concepts, Goals, and Applications
2024-02-06 13:00 - 14:00
Frontiers in Biostatistics Seminar
Tuesday February 6, 2024 @ 1pm ET
Center for Life Sciences Building, Room 11081
You Tube Link
Donna Spiegelman, ScD
Susan Dwight Bliss Professor of Biostatistics and Professor of Cardiovascular Medicine, Yale School of Medicine Professor, Department of Statistics and Data Science, Yale University
This 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.

Reconstructing the Chronology of Somatic Events From Precursor Conditions to Advanced Stage Myeloma
2024-02-07 12:00 - 13:00
Elevate @ Eleven: Comp Bio Connections
Wednesday February 7th, 2024
12:00-1:00PM
In-person only, Zelen Commons, 11th floor of the Center for Life Sciences Building
Mehmet Samur, Senior Research Scientist, Department of Data Science
Lunch is provided.

Call for Abstracts: Health Disparities and Cancer
2024-03-01
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.

Friday April 5, 2024
1:00-5:30PM ET
Yawkey Conference Center
450 Brookline Ave, Boston, MA
Registration for in person and virtual attendance.
Invited speakers:

Adewole Adamson, MD, MPP, Dell Medical School at the University of Texas at Austin
Cynthia Dwork,PhD, Harvard University
Nate Fillmore,PhD, VA Boston Healthcare System and Harvard Medical School
Carmen E. Guerra, MD, MSCE, University of Pennsylvania
Jinani Jayasekera, PhD, National Institute on Minority Health and Health Disparities
Bogdan Pasaniuc,PhD, University of California Los Angeles

Frontiers in Biostatistics: A Framework for Personalizing the Timing of Surveillance Testing
2024-03-12 13:00 - 14:00
**This seminar has been canceled. A new date will be announced shortly**
Frontiers in Biostatistics Seminar
Tuesday March 12, 2024 @ 1pm ET

Aasthaa Bansal, PhD
Associate Professor, The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington
Joint Associate Professor, Public Health Sciences Division
Joint Associate Professor, Hutchinson Institute for Cancer Outcomes Research (HICOR)
Fred Hutch Cancer Center
Abstract: 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.

2024 Marvin Zelen Symposium: Health Disparities and Cancer
2024-04-05 13:00 - 17:00 | Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, United States
Friday April 5, 2024
1:00-5:00PM ET
Yawkey Conference Center
450 Brookline Ave, Boston, MA
In-person event only, the recording will be available post-event.
Registration for in person attendance.
Invited speakers:

Adewole Adamson, MD, MPP, Dell Medical School at the University of Texas at Austin, “AI and Melanoma Disparities”
Cynthia Dwork,PhD, Harvard University, “Individuals, Groups, Indistinguishability, and Loss: 15 Years of Algorithmic Fairness in 20 Minutes”
Nate Fillmore,PhD, VA Boston Healthcare System and Harvard Medical School, “Health Disparities and Cancer in the National VA Healthcare System”
Carmen E. Guerra, MD, MSCE, University of Pennsylvania, “The (Mis)use of Race and Ethnicity in Biomedical Research”
Jinani Jayasekera, PhD, National Institute on Minority Health and Health Disparities, “Personalized Clinical Decision Tools to Support Equitable Breast Cancer Care”
Bogdan Pasaniuc,PhD, University of California Los Angeles, “Polygenic risk scoring in personalized medicine: promises and challenges”

Data Integration in Statistical Inference and Risk Prediction
2024-04-11 16:00 - 17:00
Joint Seminar with Harvard TH Chan School of Public Health
Thursday April 11, 2024 @ 4pm ET
FXB Building Room 301
Yu Shen PhD
Conversation with a Living Legend Professor & Chair ad interim
Department of Biostatistics
The University of Texas MD Anderson Cancer Center
In 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.

Deconvolving Clonotype from High-Throughput Perturb-Seq and Novel Splice Identification of Prematurely Terminated mRNA
2024-04-17 12:00 - 13:00
Elevate @ Eleven CompBio Connections
Wednesday April 17th, 2024
12:00 – 1:00 PM
Zelen Commons, Center for Life Sciences Building
Jared Brown, PhD, Research Fellow, Data Science

Lunch is provided.

Identifying Transcription Factor Binding using Open Chromatin, Transcriptome, and Methylation Data
2024-05-01 12:00 - 13:00
Elevate @ Eleven CompBio Connections
Wednesday May 1st, 2024
12:00 – 1:00 PM
Zelen Commons, Center for Life Sciences Building
Michael Hoffman, PhD, Senior Scientist and Chair, Princess Margaret Cancer Centre and Associate Professor, University of Toronto
Lunch is provided.

Cancer Risk Prediction, Early Detection and Minimal Residual Disease
2024-05-07 13:00 - 14:00
Frontiers in Biostatistics Seminar
Tuesday May 7, 2024 @ 1pm ET
In-person and Zoom available.
Cristian Tomasetti, Ph.D.
Director, Center for Cancer Prevention and Early Detection, City of Hope
Professor and Director, Division of Mathematics for Cancer Evolution and Early Detection, Department of Computational & Quantitative Medicine, City of Hope

Artificial Intelligence Methods in Cancer
2024-05-29 12:00 - 15:30
DF/HCC Cancer Data Sciences Lunch Workshop
Step into the future of oncology where our speakers will discuss the cutting-edge intersection of AI and cancer research. This workshop is geared towards researchers and clinicians who are eager to delve into the innovative methodologies that AI brings to the battle against cancer. Join us, learn, and contribute to a future where AI and human ingenuity converge to save lives.
Sponsored by the Cancer Data Sciences Program at the Dana-Farber Harvard Cancer Center and the Harvard Chan Bioinformatics Core.
Speakers:

Jason M. Johnson, PhD, Senior Vice President, Chief Data and Analytics Officer, Dana-Farber Cancer Institute
William Lotter, PhD Assistant Professor, Harvard Medical School and Dana-Farber Cancer Institute
Michael Rosenthal, MD, PhD Investigator, Hale Family Center For Pancreatic Cancer Research, Dana-Farber Cancer Institute, Assistant Professor, Harvard Medical School
Caroline Uhler, Full Professor, Department of Electrical Engineering and Computer Science and Institute for Data, Systems and Society, Massachusetts Institute of Technology
Eliezer Van Allen, MD Associate Professor, Harvard Medical School, Chief of the Division of Population Sciences, Dana-Farber Cancer Institute
Kun-Hsing Yu, MD, PhD Assistant Professor of Biomedical Informatics Harvard Medical School

Register for this hybrid event by May 24.

Bioconductor Conference 2024: Where Software and Biology Meet
2024-07-24 08:00 - 2024-07-26 17:00
July 24-26, 2024
Van Andel Institute
Grand Rapids, Michigan USA
Registration open now!
Welcome to BioC2024 – Your Bioinformatics Community Awaits
Quick Reasons to Join Us

For Everyone in Bioinformatics: Whether you’re deep in research or just starting out, BioC2024 has something for you. Connect, learn, and share in a space that welcomes every level of curiosity and expertise.
Connect Across the Globe: With our Hopin platform, you’re not just attending a conference; you’re joining a worldwide community ready to exchange ideas and forge new collaborations.
Learn From the Best: Our lineup includes some of the most forward-thinking minds in bioinformatics. Expect insights that could change how you see your work or study.
Diversity Drives Us: We’re proud to host a diverse and inclusive event. Your unique perspective enriches our community, and we strive to make BioC2024 accessible to all.

What’s in Store

Talks That Inspire: From quick insights to deep dives, our sessions are designed to spark new ideas and provide practical knowledge.
Hands-On Experience: Enhance your skill set with workshops and demos that put theory into practice. It’s learning made relevant, regardless of your experience level.
Shaped by You: Your feedback has been our guide. Expect more interactive opportunities, a broader range of topics, and even better virtual engagement.

Join the BioC2024 Experience
BioC2024 is more than a conference – it’s a chance to grow, connect, and contribute to the bioinformatics field. Don’t miss out. Secure your spot now and be ready to be inspired.

Challenges of Producing High-Quality Labelled Data
2024-08-13 13:00 - 14:00
Tuesday, August 13 @ 1:00PM Eastern Time
Center for Life Sciences Building, 11th floor, room 11081
3 Blackfan Circle, Boston
Peter Lipman
Technical Lead & Manager
Data Science & Human Computation, Google
Zoom: https://bit.ly/plipman

Fall schedule to be announced
2024-08-30 08:00 - 17:00
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.

Hierarchical Causal Models
2024-09-12 16:00 - 17:00
HSPH Biostatistics and DFCI Data Science Colloquium Series
Thursday September 12, 2024
4:00-5:00PM
HSPH FXB Building Room 301
David Blei, PhD
Professor of Statistics and Computer Science
Columbia University
Analyzing 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.
As examples, we will study how to identify and estimate causal effects under unmeasured confounders, interference, and instruments.
This is joint work with Eli Weinstein.

Inference for Treatment-Specific Survival Curves using Machine Learning
2024-09-19 16:00 - 17:00
HSPH Biostatistics and DFCI Data Science Colloquium Series
Thursday September 19, 2024
4:00-5:00PM
HSPH FXB Building Room 313
Ted Westling, Assistant Professor, Department of Mathematics & Statistics, University of Massachusetts Amherst
In 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.

Call for abstracts: DF/HCC Celebration of Early Career Investigators
2024-10-11 16:00 - 17:00
The Dana-Farber/Harvard Cancer Center will be holding its annual Celebration of Early Career Investigators on December 3rd, 2024 from 1:00 – 5:00 PM in-person at the Dana-Farber Cancer Institute.
We encourage students, postdocs, residents, clinical fellows, and instructors to submit abstracts for consideration as a short talk or as part of the PATHFINDER sponsored poster presentation by October 11th.

To submit an abstract, please visit: https://bit.ly/ECIS2024Abstract
To register to attend the event, please visit: https://bit.ly/ECIS2024
For more information, please visit our event website.

Empirical Bayes Matrix Factorization, and Genomic Applications
2024-10-18 15:00 - 16:00
Data Science Seminar
Friday, October 18, 3:00 PM ET 
Center for Life Sciences Building, 11th Floor
Matthew Stephens, PhD
Chair, Department of Statistics; Ralph W. Gerard Professor of Statistics, Human Genetics, University of Chicago
