Journal Club

Purpose & Plan

The purpose is to widen the scope for understanding papers and to provide a new networking opportunity for IBS members through a regular internet forum.

The IBS Education Committee will choose a recent paper published in the Biometrics or JABES journal. The paper may highlight an interesting application or a new methodology or development in biometrics, biostatistics or biomathematics. The papers will be chosen by the Journal Club organizer, Gen Li, in coordination with the Education Committee, and shall be of wide interest among members.

The author of the paper will be asked to make a 45-minute presentation on the paper and its importance. A discussant will also be identified to highlight the main points and raise some relevant questions for a maximum time of 15 minutes. The discussion will be opened up to participants for comments, questions and responses to the paper, under the direction of the Chair. IBS members may want to email and request an opportunity to speak about the paper, but the wider audience will be able to contribute to the discussion.

For more information about the Journal Club, contact Gen Li, Journal Club Chair or Heidi Lapka, IBO, Staff Liaison.

If you are having issues accessing member-only content please contact the International Biometric Office (IBO).

Upcoming Journal Club Session

13 February 2026 | 11:00 a.m. ET | 1600 UTC
"SPLasso for High-dimensional Additive Hazards Regression with Covariate Measurement Error" 
(Biometrics, December 2025)
Speaker: Jinfeng Xu, PhD, Associate Professor, City University of Hong Kong
Discussant: Grace Y. Yi, Professor, University of Western Ontario

Abstract
High-dimensional error-prone survival data are prevalent in biomedical studies, where numerous clinical or genetic variables are collected for risk assessment. The presence of measurement errors in covariates complicates parameter estimation and variable selection, leading to non-convex optimization challenges. We propose an error-in-variables additive hazards regression model for high-dimensional noisy survival data. By employing the nearest positive semi-definite matrix projection, we develop a fast Lasso approach (semi-definite projection Lasso, SPLasso) and its soft thresholding variant (SPLasso-T), both with theoretical guarantees. Under mild assumptions, we establish model selection consistency, oracle inequalities, and limiting distributions for these methods. Simulation studies and two real data applications demonstrate the methods’ superior efficiency in handling high-dimensional data, particularly showcasing remarkable performance in scenarios with missing values, highlighting their robustness and practical utility in complex biomedical settings.

Register Today!