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Image-based Lagrangian Particle Tracking in Bed-load Experiments
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Published on: July 20, 2017

Bayesian lead time estimation for the Johns Hopkins Lung Project data.

Hyejeong Jang1, Seongho Kim, Dongfeng Wu

  • 1Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA. h0jang01@louisville.edu

Journal of Epidemiology and Global Health
|August 13, 2013
PubMed
Summary

Estimating lead time in lung cancer screening is crucial for survival benefits. This study introduces a new method treating lifetime as a random variable, offering insights for improved screening programs.

Keywords:
Lead timeLifetime distributionLung cancerX-ray screening

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Area of Science:

  • Medical Imaging and Diagnostics
  • Biostatistics
  • Public Health

Background:

  • Lung cancer screening via X-rays remains controversial due to uncertainty about survival benefits.
  • Effective treatment following early detection is paramount for realizing screening benefits.
  • Assessing the projected lead time in lung cancer screening programs is essential for evaluating their efficacy.

Purpose of the Study:

  • To estimate the projected lead time for participants in a lung cancer screening program.
  • To analyze the impact of screening frequency and age on lead time and early detection probabilities.
  • To introduce a novel method for lead time estimation considering lifetime as a random variable.

Main Methods:

  • Applied a newly developed method for lead time estimation, treating lifetime (T) as a random variable.
  • Utilized actuarial life tables from the U.S. Social Security Administration to establish lifetime distribution.
  • Projected lead time distribution using data from the Johns Hopkins Lung Project (JHLP).

Main Results:

  • For male heavy smokers, the probability of no-early-detection with semiannual screens ranged from 32.16% to 33.17% across initial screening ages 50-70.
  • Mean lead time varied from 1.23 to 1.36 years, decreasing with increased screening intervals and initial age.
  • Increased screening intervals monotonically increased the probability of no-early-detection.

Conclusions:

  • The mean lead time estimated using a random lifetime variable is slightly lower than with a fixed lifetime value.
  • Findings are expected to contribute to the enhancement of current lung cancer screening programs.
  • The study provides valuable statistical insights into the effectiveness of lung cancer screening protocols.