Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan
Journal
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
Journal Volume
31
Journal Issue
12
Date Issued
2022-12-05
Author(s)
Chien, Li-Hsin
Chen, Tzu-Yu
Chen, Chung-Hsing
Hsiao, Chin-Fu
Chang, Gee-Chen
Tsai, Ying-Huang
Su, Wu-Chou
Huang, Ming-Shyan
Chen, Yuh-Min
Wang, Chih-Liang
Chen, Chih-Yi
Hung, Hsiao-Han
Jiang, Hsin-Fang
Hu, Jia-Wei
Rothman, Nathaniel
Lan, Qing
Liu, Tsang-Wu
Chen, Chien-Jen
Chang, I-Shou
Hsiung, Chao A
Abstract
Methods synthesizing multiple data sources without prospective datasets have been proposed for absolute risk model development. This study proposed methods for adapting risk models for another population without prospective cohorts, which would help alleviate the health disparities caused by advances in absolute risk models. To exemplify, we adapted the lung cancer risk model PLCOM2012, well studied in the west, for Taiwan.
Type
journal article