Recent Methodological Developments and Applications in Statistical and Machine Learning Approaches for Predictive Modeling Using Competing Risk Data — Topic-Contributed Papers
Section on Risk Analysis, Section on Statistical Consulting, Section on Statistics in Epidemiology
Organizer(s): Nilotpal Sanyal, Stanford University School of Medicine
Chair(s): Maya Mathur, Stanford University
10:05 AM
Scalable Algorithms for Large-Scale Competing Risks Data Eric Kawaguchi, University of Southern California; Jenny Shen, University of California Los Angeles; Gang Li, University of California Los Angeles; Marc Suchard, University of California Los Angeles
Assessing the Predictive Performance of Biomarkers in the Presence of Competing Risks Due to Transplantation
Cuihong Zhang, The University of Texas Health Science Center at Houston; Jing Ning, The University of Texas MD Anderson Cancer Center; Steven Belle, University of Pittsburgh; Robert Squires, University of Pittsburgh; Jianwen Cai, The University of North Carolina at Chapel Hill; Ruosha Li, The University of Texas Health Science Center at Houston
Challenging Collaborations and Lessons Learned — Topic-Contributed Panel
Section on Statistical Consulting, Section on Statistics and Data Science Education, Section on Statistical Learning and Data Science, Caucus for Women in Statistics
Organizer(s): Harry Dean Johnson, Washington State University
Manisha Desai, Departments of Medicine and Biomedical Data Science Stanford University Elaine Eisenbeisz, OMEGA STATISTICS Kim Love, K. R. Love QCC Clark Kogan, Washington State University NAYAK L POLISSAR, The Mountain-Whisper-Ligtht: Statistics & Data Science
Elevating Applied Statistics in Academic Departments — Invited Panel
Section on Statistical Consulting, Committee on Applied Statisticians, Section on Teaching of Statistics in the Health Sciences, Caucus for Women in Statistics
Organizer(s): Julia Sharp, Colorado State University
Chair(s): Emily Griffith, North Carolina State University
Erin Blankenship, University of Nebraska Lincoln Rebecca Doerge, Carnegie Mellon University Sandrine Dudoit, University of California Berkeley Montserrat Fuentes, University of Iowa Heather Smith, Cal Poly, San Luis Obispo
What Is the Value of Collaborative Statistics in Academia? Charlotte Allison Bolch, Midwestern University; Margaret Stedman, Stanford University; C. Christina Mehta, Emory University; Terrie Vasilopoulos, University of Florida, College of Medicine; Todd Coffey, Idaho College of Osteopathic Medicine
Building and Implementing an Effective Quantitative Sciences Internship Program for Undergraduate Students — Invited Panel
Section on Statistical Consulting, International Indian Statistical Association, Section on Statistics and Data Science Education, Section on Teaching of Statistics in the Health Sciences
Organizer(s): Kay See Tan, Memorial Sloan Kettering Cancer Center
Kay See Tan, Memorial Sloan Kettering Cancer Center Justine Herrera, Columbia University Nathan Tintle, Dordt University Heather Mattie, Harvard University Alisa Stephens-Shields, University of Pennsylvania
Kaleab Abebe, University of Pittsburgh School of Medicine Walter Ambrosius, Wake Forest School of Medicine Garnet Anderson, Fred Hutchinson Cancer Research Center Christopher Coffey, University of Iowa College of Public Health Valerie Durkalski-Mauldin, Medical University of South Carolina
Joint Modeling Bat Assemblages Using Automatic Recording Devices Meridith L Bartley, USGS; Kathryn M Irvine, US Geological Survey; Rogelio M. Rodriguez, Oregon State University - Cascades; Tomas J. Rodhouse, NPS - Upper Columbia Basin Network; Benjamin D. Neece, Oregon State University - Cascades
