Developing federated time-to-event scores using heterogeneous real-world survival data.

Siqi Li¹, Ziwen Wang², Yuqing Shang², Qiming Wu², Chuan Hong³, Yilin Ning², Di Miao², Marcus Eng Hock Ong⁴, Bibhas Chakraborty⁵, Nan Liu⁶

¹Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore. Electronic address: siqili@u.duke.nus.edu.

²Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.

³Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.

⁴Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Health Services Research Centre, Singapore Health Services, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore.

⁵Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Department of Statistics and Data Science, National University of Singapore, Singapore.

⁶Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; NUS Artificial Intelligence Institute, National University of Singapore, Singapore. Electronic address: liu.nan@duke-nus.edu.sg.

PubMed DOI

Abstract

OBJECTIVE: Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain disease or death) for patients is crucial for clinical decision-making. Scoring systems are widely used for swift and efficient risk prediction. However, existing methods for constructing survival scores presume t…

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