Dec93:00pm - 4:00pm121 South Main Street, Room 245
Ludovic Trinquart PhD, Assistant Professor of Biostatistics, Boston University School of Public Health
“Counterfactual mediation analysis with illness-death model for right-censored surrogate and clinical outcomes”
We introduce a counterfactual-based mediation analysis for surrogate outcome evaluation when both the surrogate and clinical endpoint are subject to right-censoring. We use a multistate model for risk prediction to account for both direct transitions towards the clinical endpoint and transitions through the surrogate endpoint. We use the counterfactual framework to define natural direct and indirect effects and the proportion of the treatment effect on the clinical endpoint mediated by the surrogate endpoint. We define these quantities for the cumulative risk and restricted mean survival time. We illustrate our approach using 18-year follow-up data from the SPCG-4 randomized controlled trial of radical prostatectomy for prostate cancer. We assess time to metastasis as a potential surrogate outcome for all-cause mortality.
Mar93:00pm - 4:00pm121 South Main Street, Room 245
Sumithra Mandrekar, PhD, Professor of Biostatistics and Oncology, Mayo Clinic; Section Head, Cancer Center Statistics at the Mayo Clinic Department of Health Sciences Research, Rochester, MN
Title and Abstract to be announced
Mar163:00pm - 4:00pm121 South Main Street, Room 245
Andrey Fedorov, PhD, Assistant Professor in Radiology, Surgical Planning Laboratory, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School
Abstract to be posted
Title: Managing Data for Medical Image Computing Research
Bio: Andrey joined SPL in 2009 after obtaining his PhD in Computer Science from The College of William and Mary in Virginia. His research is in translation and validation of medical image computing technology in clinical research applications, with the focus on quantitative imaging, imaging informatics and image-guided interventional procedures. Andrey is committed to advancing the role of reproducible science, data sharing and open source software in academic research. He has contributed to several open source projects, most notably 3D Slicer (http://slicer.org ) and dcmqi (https://github.com/qiicr/dcmqi ).
Together with Ron Kikinis, he served as a co-PI of the Quantitative
Image Informatics for Cancer Research (QIICR) project
(http://qiicr.org ) funded by NCI. Andrey is a technical lead for the
consortium developing Imaging Data Commons (IDC) node for the NCI Cancer Research Data Commons (CRDC) platform. The IDC will be a cloud-based resource that connects researchers with 1) cancer image collections, 2) a robust infrastructure that contains imaging data, subject and sample metadata and experimental metadata from disparate sources, 3) resources for searching, identifying and viewing images, and 4) additional data types contained in other Cancer Research Data Commons nodes.
Apr63:00pm - 4:00pm121 South Main Street, Room 245
Daniel Almirall, PhD, Associate Professor, Co-Director, Data Science for Dynamic Intervention Decision-making Laboratory (d3lab), Survey Research Center, Institute for Social Research; Department of Statistics, College of Literature Sciences and the Arts, University of Michigan
Bio: Daniel Almirallis Associate Professor in the Institute for Social Research and the Department of Statistics at the University of Michigan. He is a methodologist and statistician who develops methods to form evidence-based adaptive interventions. Adaptive interventions can be used to inform individualized intervention guidelines for the on-going management of chronic illnesses or disorders such as drug abuse, depression, anxiety, autism, obesity, or HIV/AIDS. More recently, Dr. Almirall has been developing methods to form adaptive implementation interventions, to inform how best to tailor sequences of organizational-level strategies to improve the implementation of evidence-based practices. His work includes the development of approaches related to the design, execution, and analysis of sequential multiple assignment randomized trials (SMARTs) which can be used to build adaptive interventions, and of clustered SMARTs to build adaptive implementation interventions. He is particularly interested in applications in child and adolescent mental health research.
Abstract to be posted
Apr203:00pm - 4:00pm121 South Main Street, Room 245
Bonnie Ghosh-Dastidar, PhD, Senior Statistician, RAND Corporation, Head of the RAND Statistics Group
Title: “Outcome-Optimized Nonresponse Weighting”
Abstract to be posted