Our youtube channel: https://www.youtube.com/@JohnsHopkinsSLAM
Onsite seminar
Time: 9/19/2025, Friday 1:30-2:45pm
Place: Genome Cafe, Room E3609
Light lunch provided at 1:10pm
zoom https://jh.zoom.us/j/94785266630
Speaker: Quentin Le Coent, Postdoctoral Fellow, Johns Hopkins School of Medicine
Title: Index Date Imputation for Externally Controlled Trials
Abstract: Externally controlled trials (ECTs) compare outcomes between a single-arm trial and external-controls drawn from sources such as historical trials, registries, or observational studies. In survival analysis, a major challenge arises when the time origin (index date) differs across groups. For example, when treatment initiation occurs after a delay in the single-arm trial but is undefined in the external controls. This misalignment can bias treatment effect estimates. In this work we propose a statistical method, Index Date Imputation (IDI), that imputes comparable index dates for external control patients using the estimated distribution of treatment initiation times from the single-arm cohort. To address potential confounding due to lack of randomization, IDI is combined with propensity-score methods. We evaluate its performances through a simulations study. Applying IDI to a randomized oncology trial, we demonstrate that the method recovers the known treatment effect despite artificial index date misalignment. IDI provides a principled framework for time-to-event analyses in ECTs and is broadly applicable in oncology and rare disease settings.
Upcoming events
11/7/2025
Ronghui (Lily) Xu, Professor, University of California, San Diego
11/21/2025
Gary Hettinger, Assistant Professor of Biostatistics at the NYU Grossman School of Medicine
12/5/2025
Lu Xia, Assistant Professor, Department of Statistics and Probability, Michigan State University.
Postdoctoral Fellow in Biostatistics – Johns Hopkins University
Position Description The Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and the Sidney Kimmel Comprehensive Cancer Center (SKCCC) invite applications for a full-time postdoctoral fellow position in biostatistics. The successful candidate will be jointly supervised by Dr. Mei-Cheng Wang (https://publichealth.jhu.edu/faculty/733/mei-cheng-wang) and Dr. Chen Hu (https://profiles.hopkinsmedicine.org/provider/chen-hu/2777794), and will work on cutting-edge research projects related to biomarker development and evaluation in oncology and Alzheimer’s disease.
This position offers a unique opportunity to engage in both methodological and collaborative research, focusing on survival analysis, longitudinal data analysis, and biomarker-based risk modeling. The fellow will work with high-quality clinical and observational datasets and contribute to interdisciplinary research teams at the forefront of cancer and neurodegenerative disease research.
Qualifications
· PhD in Biostatistics, Statistics, or a related quantitative field (completed by start date)
· Strong background in survival and longitudinal data analysis
· Demonstrated interest and/or experience in biomarker development and evaluation
· Proficiency in statistical programming (e.g., R, SAS, or Python)
· Excellent oral and written communication skills
· Ability to work independently and collaboratively
Position Details
· Location: On-site at Johns Hopkins University, Baltimore, MD
· Start Date: Immediate, applications will be reviewed on a rolling basis until filled
· Duration: 1 year with the possibility of renewal based on performance and funding
· Affiliation: Joint between the Department of Biostatistics (Bloomberg School of Public Health) and the Sidney Kimmel Comprehensive Cancer Center (SKCCC)
· Salary: Commensurate with NIH postdoctoral stipend levels and candidate experience
Application Instructions To apply, please send the following documents to huc@jhu.edu and mcwang@jhu.edu:
1. A cover letter describing your research interests and career goals
2. Curriculum vitae
3. Names and contact information for three references
4. One or two representative publications or manuscripts (if available)