Meeting Calendar

Virtual seminar

Time: 11/7/2025, Friday 1:30-2:45pm

Place: Genome Cafe, Room E3609

Speaker: Ronghui Lily Xu, Professor, Department of Family and Preventive Medicine and the Department of Mathematics, University of California, San Diego

Title: Learning Treatment Effects under Covariate Dependent Left Truncation and Right Censoring

[Abstract] Time-to-event outcomes are often subject to left truncation and right censoring. While many survival analysis methods have been developed to handle truncation and censoring, majority of the past works require strong independence assumptions. We relax these assumptions through leveraging covariate information together with orthogonal learning, and develop a liberating framework from left truncation and right censoring so that desirable properties like double robustness can be immediately transferred from settings without truncation or censoring. To illustrate its generality and ease to use, the framework is applied to estimation of the average treatment effect (ATE) and the conditional average treatment effect (CATE). For the ATE, we establish both model and rate double robustness under confounding, truncation and censoring; for the CATE, we show that the orthogonal and the doubly robust learners under these three sources of bias can achieve oracle rate of convergence. We study the estimators both theoretically and through extensive simulation, and apply them to analyzing the effect of mid-life heavy drinking on late life cognitive impairment free survival, using data from the Honolulu Asia Aging Study.

Upcoming events

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)

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