Journal of Biometrics & Biostatistics

Over-diagnosis in Lung Cancer Screening using the MSKC-LCSP Data

Abstract

Yu-Ting Chen, Diane Erwin and Dongfeng Wu

We applied a newly developed probability method to predict long term outcomes and over diagnosis in lung cancer screening using the Memorial Sloan-Kettering Cancer study (MSKC-LCSP) data. All participants were categorized into four mutually exclusive groups depending on their diagnosis status and ultimate disease status: symptom-freelife, no-early-detection, true-early-detection and over-diagnosis. Probability of each group is a function of the three key parameters: screening sensitivity, sojourn time in preclinical state and transition density from disease free to the preclinical state. We first obtained reliable and accurate estimates of these three key parameters using the MSKCLCSP data and likelihood function with a Bayesian approach, and then calculate the probability of each group by inserting these Bayesian posterior samples to the probability formulae, to predict future long term outcomes of lung cancer screening using chest x-ray. Human lifetime was treated as a random variable derived from US. Social Security Administration (SSA), so number of screening exams in the future is a random variable as well. The result shows that over diagnosis is not a big issue in lung cancer screening, given that it is only about 4.56% to 7.43% among the screendetected cases, depending on the age at the first screening.