A Statistical Method to Schedule the First Exam Using Chest X-Ray in Lung Cancer Screening

Farhin Rahman and Dongfeng Wu ()
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Farhin Rahman: Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, 485 E. Gray St., Louisville, KY 40202, USA
Dongfeng Wu: Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, 485 E. Gray St., Louisville, KY 40202, USA

Mathematics, 2025, vol. 13, issue 16, 1-13

Abstract: We applied a recently developed statistical method to the National Lung Screening Trial (NLST) chest X-ray data, to find the optimal time for initiating chest X-ray screening in asymptomatic individuals. Incidence probability was used to control the risk of clinical incidence before the first exam, constraining it to a small value, given one’s current age. The simulation study shows that the optimal screening age remains relatively consistent as the current age increases. Notably, male heavy smokers tend to have slightly later screening ages compared to females, which contrasts with findings from the NLST study using computed tomography (CT) scans. After the future screening time/age is found, we can estimate the lead time distribution and the probability of overdiagnosis if one would be diagnosed at this future time/age. The lead time is relatively consistent across incidence probability and sensitivity, with a slight decrease in the mean lead time as the current age increases, and it is positively correlated with the sojourn time. The probability of overdiagnosis exhibits positive correlations with the mean sojourn time, incidence probability, and the current age, with only slight changes in sensitivity. Overall, the probability of overdiagnosis is small and is not a concern at a younger age.

Keywords: lung cancer; incidence probability; overdiagnosis; lead time; sensitivity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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