Evidence map›Paper›PMID 21075475›Full record

ArticleLung cancer (Amsterdam, Netherlands)2011

Sojourn time and lead time projection in lung cancer screening.

Dongfeng Wu, Diane Erwin, Gary L Rosner

Abstract read
In one paragraph

Article in Lung cancer (Amsterdam, Netherlands), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Sensitivity of chest X-ray for detecting lung cancer in people presenting with symptoms: a systematic review.The British journal of general practice : the journal of the Royal College of General Practitioners · 2019
    Pooled it
  2. Lung cancer detectability by test, histology, stage, and gender: estimates from the NLST and the PLCO trials.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2015
    Trial
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  10. When to initiate cancer screening exam?Statistics and its interface · 2022
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  14. Estimating lung cancer risk from chest X-ray and symptoms: a prospective cohort study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Dongfeng WuDept of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA. dongfeng.wu@louisville.edu
Diane Erwin
Gary L Rosner

Funding

Translational Research Central ServicesP30CA006973 · NCI · JOHNS HOPKINS UNIVERSITY · PI Rosalie Wollett · 1985 to 2026
$208.6M
Statistical Inference for Lead Time in Cancer ScreeningR03CA115012 · NCI · MISSISSIPPI STATE UNIVERSITY · PI WU, DONGFENG · 2005 to 2006
$136k
NCI NIH HHS CA-115012NCI NIH HHS P30 CA006973NCI NIH HHS R03 CA115012
6 · The paper itself

Abstract

objectivesWe 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.

methodsWe 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.

resultsThe 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.

conclusionAlthough 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.

Indexed as

Computer SimulationMass Chest X-RayAdenocarcinomaAgedBayes TheoremEarly Detection of CancerHumansLung NeoplasmsMaleMiddle AgedSensitivity and SpecificitySmokingTime Factors

Identifiers

PMID21075475
PMCPMC4839299

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.