Dataset Information

Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan.

ABSTRACT:

Background

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.

Methods

Using Taiwanese multiple data sources, we formed an age-matched case-control study of ever-smokers (AMCCSE), estimated the number of ever-smoking lung cancer patients in 2011-2016 (NESLP2011), and synthesized a dataset resembling the population of cancer-free ever-smokers in 2010 regarding the PLCOM2012 risk factors (SPES2010). The AMCCSE was used to estimate the overall calibration slope, and the requirement that NESLP2011 equals the estimated total risk of individuals in SPES2010 was used to handle the calibration-in-the-large problem.

Results

The adapted model PLCOT-1 (PLCOT-2) had an AUC of 0.78 (0.75). They had high performance in calibration and clinical usefulness on subgroups of SPES2010 defined by age and smoking experience. Selecting the same number of individuals for low-dose computed tomography screening using PLCOT-1 (PLCOT-2) would have identified approximately 6% (8%) more lung cancers than the US Preventive Services Task Forces 2021 criteria. Smokers having 40+ pack-years had an average PLCOT-1 (PLCOT-2) risk of 3.8% (2.6%).

Conclusions

The adapted PLCOT models had high predictive performance.

Impact

The PLCOT models could be used to design lung cancer screening programs in Taiwan. The methods could be applicable to other cancer models.

SUBMITTER: Chien LH

PROVIDER: S-EPMC9720426 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan.

Chien Li-Hsin LH Chen Tzu-Yu TY Chen Chung-Hsing CH Chen Kuan-Yu KY Hsiao Chin-Fu CF Chang Gee-Chen GC Tsai Ying-Huang YH Su Wu-Chou WC Huang Ming-Shyan MS Chen Yuh-Min YM Chen Chih-Yi CY Liang Sheng-Kai SK Chen Chung-Yu CY Wang Chih-Liang CL Hung Hsiao-Han HH Jiang Hsin-Fang HF Hu Jia-Wei JW Rothman Nathaniel N Lan Qing Q Liu Tsang-Wu TW Chen Chien-Jen CJ Yang Pan-Chyr PC Chang I-Shou IS Hsiung Chao A CA

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20221201 12

<h4>Background</h4>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.<h4>Methods</h4>Using Taiwanese multiple data sources, ...[more]

PMID: 36129788

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Dataset Information

Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan.

Background

Methods

Results

Conclusions

Impact

Publications

Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan.

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