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DESCRIPTION:Chixiang Chen\, Ph.D. \n\nAssociate Professor in Biostatistics
 \n	EPH\, University of Maryland School of Medicine\n\nWHEN: Wednesday\, Jan
 uary 21\, 2026\, from 3:30 to 4:30 p.m.\n	WHERE: Hybrid | 2001 91Ë¿¹ÏÊÓƵ Colle
 ge Avenue\, Rm 1140\; Zoom\n	NOTE: Chixiang Chen will be presenting virtual
 ly from Baltimore\n\nAbstract\n\nIn addition to the primary outcome\, seco
 ndary 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 coh
 ort studies or real-world data analysis (source 2). Despite being termed '
 secondary\,' these outcomes have significant potential to enhance estimati
 on and inference in primary outcome analysis. This is particularly true wh
 en the primary outcome is a summary score derived from secondary outcomes\
 , which may lack the detailed information specific to each secondary outco
 me. This talk will summarize the challenges of integrating information fro
 m 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 o
 f big data. Real data examples will be provided\, and future directions to
 ward Tool4.0 will be discussed at the end of the talk.\n\nSpeaker Bio\n\nC
 hixiang Chen is an Associate Professor in the Department of Epidemiology a
 nd Public Health at UMSOM and an affiliated faculty in the AMSC program fr
 om the Department of mathematics in UMD. Throughout his early career\, Dr.
  Chen has been devoted to advancing statistical and data science methods i
 n large-scale data (e.g.\, Medicare Claims\, UKB)\, encompassing diverse a
 reas such as causal inference\, machine learning\, information borrowing\,
  missing data analysis\, and omics data analysis. His extensive collaborat
 ions span various fields\, including aging\, gerontology\, angiology\, bio
 informatics\, biochemistry\, and neuroscience\, among others. His dedicati
 on to research has resulted in many peer-reviewed publications in prestigi
 ous journals\, including JASA\, JRSSB\, Biometrics\, etc. Dr. Chen is also
  the recipient of the 2024 Early Career Award from Association for Clinica
 l and Translational Statisticians\, honorable mention award of 2024 ICSA C
 hina conference\, and High Value Early Career Faculty Award for the 2023-2
 024 term from the National Pepper Older Americans Independence Centers (OA
 IC). He is currently the PI in multiple NIH funded projects\, including R0
 1 focusing on post-fracture recovery for older adults living with ADRD (la
 b website: https://sites.google.com/view/chixiangchen/)\n
DTSTART:20260121T203000Z
DTEND:20260121T213000Z
SUMMARY:Promising Tools for Integrating Information from Secondary Outcomes
  to Improve Primary Data Analysis: A New Usage of Secondary Outcomes in th
 e Era of Big Data
URL:/spgh/channels/event/promising-tools-integrating-i
 nformation-secondary-outcomes-improve-primary-data-analysis-new-usage-3699
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