Promising Tools for Integrating Information from Secondary Outcomes to Improve Primary Data Analysis: A New Usage of Secondary Outcomes in the Era of Big Data
Chixiang Chen, Ph.D.
Associate Professor in Biostatistics
EPH, University of Maryland School of Medicine
WHEN: Wednesday, January 21, 2026, from 3:30 to 4:30 p.m.
WHERE: Hybrid | 2001 McGill College Avenue, Rm 1140; Zoom
NOTE: Chixiang Chen will be presenting virtually from Baltimore
Abstract
In addition to the primary outcome, secondary outcomes are gaining prominence in contemporary biomedical research. These can be easily derived from traditional endpoints in clinical trials (source 1) and from compound or risk prediction scores in large-scale cohort studies or real-world data analysis (source 2). Despite being termed 'secondary,' these outcomes have significant potential to enhance estimation and inference in primary outcome analysis. This is particularly true when the primary outcome is a summary score derived from secondary outcomes, which may lack the detailed information specific to each secondary outcome. This talk will summarize the challenges of integrating information from secondary outcomes into primary data analysis and will describe recently developed tools to address these challenges. We will begin with an early version that considers only one secondary outcome (Tool1.0) and then move on to a more updated version that can handle multiple secondary outcomes (Tool2.0). Building on the first two versions, we will describe the latest version (Tool3.0), which facilitates more robust information integration in a data-driven manner and has great potential applications in the era of big data. Real data examples will be provided, and future directions toward Tool4.0 will be discussed at the end of the talk.
Speaker Bio
Chixiang Chen is an Associate Professor in the Department of Epidemiology and Public Health at UMSOM and an affiliated faculty in the AMSC program from the Department of mathematics in UMD. Throughout his early career, Dr. Chen has been devoted to advancing statistical and data science methods in large-scale data (e.g., Medicare Claims, UKB), encompassing diverse areas such as causal inference, machine learning, information borrowing, missing data analysis, and omics data analysis. His extensive collaborations span various fields, including aging, gerontology, angiology, bioinformatics, biochemistry, and neuroscience, among others. His dedication to research has resulted in many peer-reviewed publications in prestigious journals, including JASA, JRSSB, Biometrics, etc. Dr. Chen is also the recipient of the 2024 Early Career Award from Association for Clinical and Translational Statisticians, honorable mention award of 2024 ICSA China conference, and High Value Early Career Faculty Award for the 2023-2024 term from the National Pepper Older Americans Independence Centers (OAIC). He is currently the PI in multiple NIH funded projects, including R01 focusing on post-fracture recovery for older adults living with ADRD (lab website: https://sites.google.com/view/chixiangchen/)