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 AMCC

SUBMITTER: Chien LH 

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

REPOSITORIES: biostudies-literature