Sojourn time and lead time projection in lung cancer screening

Abstract

Objectives We investigate screening sensitivity, transition probability and sojourn time in lung cancer screening for male heavy smokers using the Mayo Lung Project data. We also estimate the lead time distribution, its property, and the projected effect of taking regular chest X-rays for lung cancer detection. Methods We apply the statistical method developed by Wu et al. [1] using the Mayo Lung Project (MLP) data, to make Bayesian inference for the screening test sensitivity, the age-dependent transition probability from disease-free to preclinical state, and the sojourn time distribution, for male heavy smokers in a periodic screening program. We then apply the statistical method developed by Wu et al. [2] using the Bayesian posterior samples from the MLP data to make inference for the lead time, the time of diagnosis advanced by screening for male heavy smokers. The lead time is distributed as a mixture of a point mass at zero and a piecewise continuous distribution, which corresponds to the probability of no-early-detection, and the probability distribution of the early diagnosis time. We present estimates of these two measures for male heavy smokers by simulations. Results The posterior sensitivity is almost symmetric, with posterior mean 0.89, and posterior median 0.91; the 95% highest posterior density (HPD) interval is (0.72, 0.98). The posterior mean sojourn time is 2.24 years, with a posterior median of 2.20 years for male heavy smokers. The 95% HPD interval for the mean sojourn time is (1.57, 3.35) years. The age-dependent transition probability is not a monotone function of age; it has a single maximum at age 68. The mean lead time increases as the screening time interval decreases. The standard error of the lead time also increases as the screening time interval decreases. Conclusion Although the mean sojourn time for male heavy smokers is longer than expected, the predictive estimation of the lead time is much shorter. This may provide policy makers important information on the effectiveness of the chest X-rays and sputum cytology in lung cancer early detection. Published by Elsevier Ireland Ltd.

The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer

“…Although some modellers have presumed that lung cancer develops sufficiently rapidly to produce lead times of around 2 years, [126][127][128] those actually engaged in screening have been more circumspect. Development times of 6 and 8 years between stages 1 (if screen detected) and four (if presenting) have been suggested for Japan 129 and ELCAP, 130 respectively, and these figures set the upper limits on lead times.…”

Bayesian Estimation of the Three Key Parameters in CT for the National Lung Screening Trial Data

“…sputum cytology were used. By using Mayo Lung Project male heavy smokers data, Wu et al [6] estimated combined X-rays and sputum cytology sensitivity is 89.4%. Chen et al [9] estimated the screening sensitivity of sputum cytology as a supplement to the chest X-ray using MSKC-LCSP data was 86.64%.…”

“…In the Mayo Lung Project study, the mean sojourn time was 2.2 years [6], the mean sojourn time for male heavy smokers in MSKC-LCSP data is about 3.35 years. The posterior mean sojourn in this study is around 1.5 years for both gender groups in this study.…”

“…This research proposes 30% as a reasonable upper limit for w(t). A more detailed description of the parametric models can be found in Wu et al [5,6]. We choose a different sojourn time distribution than Wu et al [5], where the previous research used log logistic, and we use Weibull distribution here, both share the same property of mathematical simplicity, and both are stable with 2 parameters.…”

Estimation of sensitivity depending on sojourn time and time spent in preclinical state

“…Wu et al3 analyzed the MLP data using the age-independent sensitivity model. For the MLP data, the posterior sensitivity had the posterior mean and median of 0.89 and 0.91, respectively, and the 95% highest posterior density (HPD) interval was (0.72, 0.98), which…”