Dept of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA.
Lung Cancer. 2011 Jun;72(3):322-6. doi: 10.1016/j.lungcan.2010.10.010. Epub 2010 Nov 13.
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.
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.
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.
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.
我们利用 Mayo 肺癌项目（MLP）的数据，研究男性重度吸烟者肺癌筛查的筛查敏感性、转移概率和逗留时间。我们还估计了领先时间分布、其性质以及定期进行胸部 X 光检查用于肺癌检测的预期效果。
我们应用 Wu 等人[1]开发的统计方法，使用 MLP 数据进行贝叶斯推断，以获得男性重度吸烟者在定期筛查计划中的筛查试验敏感性、从无疾病到临床前状态的年龄相关转移概率，以及逗留时间分布。然后，我们应用 Wu 等人[2]开发的统计方法，使用来自 MLP 数据的贝叶斯后验样本，对男性重度吸烟者的领先时间进行推断，即通过筛查提前诊断的时间。领先时间分布为零的点质量和分段连续分布的混合物，对应于无早期检测的概率和早期诊断时间的概率分布。我们通过模拟为男性重度吸烟者提供这些两个指标的估计值。
后验敏感性几乎是对称的，后验均值为 0.89，后验中位数为 0.91；95%最高后验密度（HPD）区间为（0.72，0.98）。男性重度吸烟者的后验平均逗留时间为 2.24 年，后验中位数为 2.20 年。平均逗留时间的 95%HPD 区间为（1.57，3.35）年。年龄相关转移概率不是年龄的单调函数；它在 68 岁时具有单个最大值。随筛查时间间隔的减小，平均领先时间增加。随着筛查时间间隔的减小，领先时间的标准误差也增加。
尽管男性重度吸烟者的平均逗留时间比预期的要长，但对领先时间的预测估计要短得多。这可能为决策者提供有关胸部 X 光和痰液细胞学在肺癌早期检测中的有效性的重要信息。
